Tactile sensing is pivotal for enabling effective human-robot interaction, especially in unstructured environments. This work introduces an innovative bioinspired soft robotic finger endowed with shape-adaptive and multi-modal tactile perception capabilities, drawing inspiration from diverse biological tactile sensing modalities. Through an advanced Fin Ray structure, the soft finger features tactile whiskers on its fingertips, facilitating perception of obstacle orientation, fingertip pressure, surface roughness, and grasping ball size. Leveraging distributed optical fiber sensing technology, we develop a sophisticated multi-point, multi-modal tactile perception neural network tailored for the soft finger. Meticulous integration via advanced 3D printing and silicone coating techniques seamlessly embeds optical fiber sensors within the soft robotic finger, creating an intelligent perception-capable bioinspired mechanical system. Experimental validation confirms the soft robotic finger’s sensitive and precise force perception and curvature recognition abilities, achieving accuracies of up to 100%. In summary, our bioinspired robotic finger holds significant promise for applications in intelligent sensing, non-destructive grasping, and fruit classification within unstructured environments, thus advancing the field of robotics and human-robot interaction.

Video text extraction plays an important role for multimedia understanding and retrieval. Most previous research efforts are conducted within individual frames. A few of recent methods, which pay attention to text tracking using multiple frames, however, do not effectively mine the relations among text detection, tracking and recognition. In this paper, we propose a generic Bayesian-based framework of Tracking based Text Detection And Recognition (T DAR) from web videos for embedded captions, which is composed of three major components, i.e., text tracking, tracking based text detection, and tracking based text recognition. In this unified framework, text tracking is first conducted by tracking-by-detection. Tracking trajectories are then revised and refined with detection or recognition results. Text detection or recognition is finally improved with multi-frame integration. Moreover, a challenging video text (embedded caption text) database (USTB-VidTEXT) is constructed and publicly available. A variety of experiments on this dataset verify that our proposed approach largely improves the performance of text detection and recognition from web videos.

This paper is concerned with event clustering for short text streams, which aims to divide constantly arriving short texts into several dynamic event-based clusters. A widely adopted approach is based on the Vector Space Models (VSMs) such as bag of words. However, these models have limitations in that not only the semantic relationships between words are largely ignored, the term weighting may also not well adapt the change of data stream. To avoid these limitations, in this paper, we propose a new model which captures the compositional structure of textual relations, and jointly utilize the event content similarity, temporal and location proximity to evaluate the distance between texts. We then leverage the benefits of ClusTree, a parameter-free stream clustering algorithm, to discover micro-clusters and maintain stream summaries. The experimental results on several real world data sets demonstrate the superiority of the proposed method over the state-of-the-arts.

Taking advantage of the large scale corpus on the web to effectively and efficiently mine the topics within texts is an essential problem in the era of big data. We focus on the problem of learning text topic embedding in an unsupervised manner, which enjoys the properties of efficiency and scalability. Text topic embedding represents words and documents in a semantic topic space, in which the words and documents with similar topic will be embedded close to each other. When compared with conventional topic models, which implicitly capture the document-level word co-occurrence patterns, text topic embedding alleviates the data sparsity problem and captures the semantic relevance between different words and documents. To model text topic embedding, we propose a Bidirectional Hierarchical Skip-Gram model (BHSG) based on skip-gram model. BHSG includes two components: semantic generation module to learn semantic relevance between texts and topic enhance module to produce the text topic embedding based on text embedding learned in the former module. We evaluated our method on two kinds of topic-related tasks: text classification and information retrieval. The experimental results on four public datasets and one dataset we provide all demonstrate that our proposed method can achieve a better performance.

Thin metal sheets are often located in the coupling paths of magnetic coupling energy transfer (MCET) systems. Eddy currents in the metals reduce the energy transfer efficiency and can even present safety risks. This paper describes the use of etched fractal patterns in the metals to suppress the eddy currents and improve the efficiency. Simulation and experimental results show that this approach is very effective. The fractal patterns should satisfy three features, namely, breaking the metal edge, etching in the high-intensity magnetic field region, and etching through the metal in the thickness direction. Different fractal patterns lead to different results. By altering the eddy current distribution, the fractal pattern slots reduce the eddy current losses when the metals show resistance effects and suppress the induced magnetic field in the metals when the metals show inductance effects. Fractal pattern slots in multilayer high conductivity metals (e.g., Cu) reduce the induced magnetic field intensity significantly. Furthermore, transfer power, transfer efficiency, receiving efficiency, and eddy current losses all increase with the increase of the number of etched layers. These results can benefit MCET by efficient energy transfer and safe use in metal shielded equipment.

Early stages of the human visual system consist of retinal cones, retinal ganglion cells(RGC), lateral geniculate nucleus(LGN) and V1. Modeling early visual stages is conducive to reveal the mechanism of visual signal preprocessing and representation inside brain, as well as settle challenges artificial intelligence confronts. However, a majority of previous work often models RGC/LGN or V1 separately, seldom modeling them together hierarchically. In order to be consistent with the biological results, we propose HEVS (a Hierarchical computational model for Early stages of the Visual System), a feedforward neural network composed of three layers, which represent receptor neurons, RGC/LGN and V1 successively. Exactly as the visual system, the proposed locally connected model is derived in the unsupervised scenario on natural images and trained in the bottom-up order. In order to learn the two connection weights among three layers, we formulate two optimization problems based on the reconstruction error and sparse learning. Unlike traditional models on RGC/LGN, we perform weighted similarity measuring as a regular term to simulate the strong correlations among nearby neuron spikes in the same stage. Different from existing researches on modeling V1 neurons from image pixels directly, we transmit the signals represented by the ganglion cells in the second layer to the V1 neurons in the third layer. Moreover, solutions to these objectives are provided as well. Experimental results demonstrate that the characteristics of HEVS are consistent with those of the corresponding biological stages. The results further verify the performance of HEVS on dealing with the de-blurring and de-noising tasks.

Clustering on multi-type relational data has attracted increasing interest due to its great practical and theoretical importance. One of the most popular solutions is nonnegative matrix factorization. However, previous work on nonnegative matrix factorization typically copes with multi-type relations individually, and ignores the underlying semantics conveyed by the relation propagation. Additionally, these approaches may suffer from data sparsity as most of the relations between object pairs are unknown. In this paper we propose a novel Meta-Path based Nonnegative Matrix Factorization (MPNMF) framework, which enriches potentially useful similarity semantics for the improved clustering performance. We begin with constructing meta-paths, i.e., paths that connects object types via a sequence of relations, which are appropriately weighted according to certain propagation decay rules. Based on the weighted meta-paths, we are promised to characterize the strength of pairwise interactions among the objects. Together with the attributes in the bag-of-word form, we cluster the objects of target type by collective nonnegative matrix factorization. Experiments on real world datasets demonstrate the effectiveness of our method.

Emotion entrainment accounts for the rhythmic convergence of human emotions through social interactions. This phenomenon abounds in various disciplines, i.e. effervescency in soccer games, anger proliferation in violence incidents, or anxiety diffusion in disasters. Although emotion entrainment is highly relevant to the quality of human daily life, the principles underpinning this phenomenon is still unclear. Previous dynamic models try to explain entrainment phenomenon by assuming symmetrical coupling among identical individuals. Yet this assumption clearly does not hold in real-world human interactions. As such, we propose an alternative model that captures asymmetric relationships. In depicting the coupling mechanism, the effect of social influence is also encoded. Experimental results on two emergent social events suggest that the proposed model characterizes emotion trends with high accuracy. Also, we explain the emotion dynamics by analyzing the reconstructed entrainment matrix. Our work may present practical implications for those who want to guide or regulate the emotion evolution in emergency events discussed online.

Text detection in natural scene images is an important prerequisite for many content-based image analysis tasks, while most current research efforts only focus on horizontal or near horizontal scene text. In this paper, first we present a unified distance metric learning framework for adaptive hierarchical clustering, which can simultaneously learn similarity weights (to adaptively combine different feature similarities) and the clustering threshold (to automatically determine the number of clusters). Then, we propose an effective multi-orientation scene text detection system, which constructs text candidates by grouping characters based on this adaptive clustering. Our text candidates construction method consists of several sequential coarse-to-fine grouping steps: morphology-based grouping via single-link clustering, orientation-based grouping via divisive hierarchical clustering, and projection-based grouping also via divisive clustering. The effectiveness of our proposed system is evaluated on several public scene text databases, e.g., ICDAR Robust Reading Competition data sets (2011 and 2013), MSRA-TD500 and NEOCR. Specifically, on the multi-orientation text data set MSRA-TD500, the f measure of our system is 71 percent, much better than the state-of-the-art performance. We also construct and release a practical challenging multi-orientation scene text data set (USTB-SV1K), which is available at http://prir.ustb.edu.cn/TexStar/MOMV-text-detection/.

The sociological theory of entrainment accounts for the synchronization of human rhythmic modalities through social interactions: they coordinate in a variety of dimensions including linguistic styles, facial expressions, music pace, applause, and so on. Though highly relevant, emotion entrainment has received little attention to date. In addition, most previous studies on entrainment are done through small scale or controlled laboratory studies. In this paper, we investigate emotion entrainment in the context of online social media. To the best of our knowledge, this is the first time that emotion entrainment has been examined on a large scale, real world setting. For this purpose, we propose a framework that can model entrainment phenomenon and measure its effect. Our framework differentiates from previous research by its model-free essential and discerning in entrainment directions. These traits enable us to model entrainment dynamics under few assumptions, and distinguish emotion flow of entrainment. In our studies, we investigate entrainment patterns under different emotion states, i.e. positive, neutral and negative. We discover that entrainments under different emotions all follow a power law distribution. Besides, people are willing to entrain to others under positive emotion, and users with positive emotion are more likely to be entrained. By inspecting the interactions between entrainment and emotion, we reveal that entrainment has an effect of negotiating different emotion types toward an even distribution.

Semantically searching and navigating products (e.g., on Taobao.com or Amazon.com) with professional metadata and user-generated content from social media is a hot topic in information retrieval and recommendation systems, while most existing methods are specifically designed as a purely searching system. In this paper, taking Social Book Search as an example, we propose a general search-recommendation hybrid system for this topic. Firstly, we propose a Generalized Content-Based Filtering (GCF) model. In this model, a preference value, which flexibly ranges from 0 to 1, is defined to describe a user's preference for each item to be recommended, unlike conventionally using a set of preferable items. We also design a weighting formulation for the measure of recommendation. Next, assuming that the query in a searching system acts as a user in a recommendation system, a general reranking model is constructed with GCF to rerank the initial resulting list by utilizing a variety of rich social information. Afterwards, we propose a general search-recommendation hybrid framework for Social Book Search, where learning-to-rank is used to adaptively combine all reranking results. Finally, our proposed system is extensively evaluated on the INEX 2012 and 2013 Social Book Search datasets, and has the best performance ([email protected]) on both datasets compared to other state-of-the-art systems. Moreover, our system recently won the INEX 2014 Social Book Search Evaluation.

The aim of this work is to localize a query mobile photograph by utilizing surveillance images, which naturally provide location information. We cast this cross-device visual localization problem as a classification task. By exploiting the surveillance network to collect reference images, the data acquisition process is significantly facilitated. However, the discrepancy between mobile images and surveillance images makes the training samples difficult to be used directly, and the scarcity of training samples caused by the immobility of surveillance cameras further degrades the performance. In contrast to most traditional domain adaptation problems and semi-supervised problems, the scarce labeled data and plentiful unlabeled data exist in different domains. Our location recognition method first exploits the unsupervised subspace alignment to weaken the discrepancy between the two domains, and then adopts the semi-supervised Laplacian SVM to reinforce the discriminant information utilizing the unlabeled mobile images. Experimental results show that our location recognition method significantly outperforms other related methods.

Given multiple classifiers, one prevalent approach in classifier ensemble is to diversely combine classifier components (diversity-based ensemble), and a lot of previous works show that this approach can improve accuracy in classification. However, how to measure diversity and perform diversity-based learning are still challenges in the literature. Moreover, the learning procedure highly depends upon the distribution of the training data. In this paper, we propose a novel classifier ensemble method which combines classifiers with both diversity and sample weighting. First, by designing a matrix for the (sample) data distribution creatively, we formulate a unified optimization model for diversity-based ensemble with sample weighting, where classifier weights are learned through a convex quadratic programming problem with given sample weights. Second, we propose a new self-training algorithm to iteratively run the convex optimization and automatically learn the sample weights. Moreover, these sample weights are updated with a dynamically damped learning trick, which has a good performance for convergence. This paper also discusses the relationship between our optimization model and the margin theory. Extensive experiments on a variety of 50 UCI classification benchmark data sets show that the proposed approach consistently outperforms conventional ensembles such as Bagging, GASEN, and SDP.

After being kick-started with major breakthrough in 2006 by Hinton, LeCun and Bengio respectively, deep learning has been becoming the mainstream for challenging classification systems, which, however always were with "shallow" discriminative classifiers in the past. In this paper, we argue that in common classification cases with plenty but not enough training examples, mixed-quality examples for dozens of categories, deep learning and shallow classification may have complementary performance. Then, we design a hybrid recognition strategy with classification switching to adaptively fuse deep learning and shallow classification technologies. Finally, we present a variety of experiments with visual recognition tasks, i.e., USPS character recognition, Caltech101 visual object classification, and ICDAR scene text recognition. Specifically, we perform word recognition by dynamically combing the conventional open source OCR engine with the present popular convolutional neural networks, and construct an effective end-to-end scene text recognition system with open-vocabulary. This end-to-end system is evaluated on ICDAR 2011 Robust Reading Competition (Challenge 2) dataset, the f measure of which is 54.5%, much better than 45.2% of the latest state-of-the-art performance.

Text localization in scene images is an essential and interesting task to analyze the image contents. In this work, a Bayesian network scores using K2 algorithm in conjunction with the geometric features based effective text localization method with the help of maximally stable extremal regions (MSERs). First, all MSER-based extracted candidate characters are directly compared with an existing text localization method to find text regions. Second, adjacent extracted MSER-based candidate characters are not encompassed into text regions due to strict edges constraint. Therefore, extracted candidate character regions are incorporated into text regions using selection rules. Third, K2 algorithm-based Bayesian networks scores are learned for the complimentary candidate character regions. Bayesian logistic regression classifier is built on the Bayesian network scores by computing the posterior probability of complimentary candidate character region corresponding to non-character candidates. The higher posterior probability of complimentary Candidate character regions are further grouped into words or sentences. Bayesian networks scores based text localization system, named as BayesText, is evaluated on ICDAR 2013 Robust Reading Competition (Challenge 2 Task 2.1: Text Localization) database. Experimental results have established significant competitive performance with the state-of-the-art text detection systems.

In this paper, we propose a method for short text categorization using topic model and integrated classifier. To enrich the representation of short text, the Latent Dirichlet Allocation (LDA) model is used to extract latent topic information. While for classification, we combine two classifiers for achieving high reliability. Particularly, we train LDA models with variable number of topics using the Wikipedia corpus as external knowledge base, and extend labeled Web snippets by potential topics extracted by LDA. Then, the enriched representation of snippets are used to learn Maximum Entropy (MaxEnt) and support vector machine (SVM) classifiers separately. Finally, viewing that the most possible predicted result will appear in the top two candidates selected by MaxEnt classifier, we develop a novel scheme that if the gap between these candidates is large enough, the predicted result is considered to be reliable; otherwise, the SVM classifier will be integrated with MaxEnt classifier to make a comprehensive prediction. Experimental results show that our framework is effective and can outperform the state-of-the-art techniques.

The rationale of XML design is to transfer and store data at different levels. A key feature of these levels in an XML document is to identify its components for additional processing. XML components can expose sensitive information after application

Text detection in natural scene images is an important prerequisite for many content-based image analysis tasks. In this paper, we propose an accurate and robust method for detecting texts in natural scene images. A fast and effective pruning algorithm is designed to extract Maximally Stable Extremal Regions (MSERs) as character candidates using the strategy of minimizing regularized variations. Character candidates are grouped into text candidates by the single-link clustering algorithm, where distance weights and clustering threshold are learned automatically by a novel self-training distance metric learning algorithm. The posterior probabilities of text candidates corresponding to non-text are estimated with a character classifier; text candidates with high non-text probabilities are eliminated and texts are identified with a text classifier. The proposed system is evaluated on the ICDAR 2011 Robust Reading Competition database; the f-measure is over 76%, much better than the state-of-the-art performance of 71%. Experiments on multilingual, street view, multi-orientation and even born-digital databases also demonstrate the effectiveness of the proposed method.

In ensemble learning, a higher accuracy can be achieved by integrating some classifiers instead of all the classifiers. But, it is very difficult to select the best classifier combination which can be seen as an optimization problem, from a pool of classifiers. To deal with this problem, we propose a new classifier selection method, Sorting-based Dynamic Classifier Ensemble Selection (SDES), which consists of two stages: (1) classifier sorting, and (2) dynamic ensemble selection on sorted classifier sequence. In the first stage, classifiers are sorted based on diversity, to avoid searching for the nearest neighbors in dynamic ensemble selection methods and greatly improve the selection efficiency. In the second stage, the optimal subset of classifiers is selected from the sorted classifier sequence based on confidence of test samples, to guarantee high accuracy of the optimal classifier subset. Experimental results have shown the effectiveness and high efficiency of the proposed method.

XML (eXtensible Markup Language) is a standard and entirely user-driven language for storage and transfer of information. XML frequent itemsets are usually found for mining XML association rules from XML transactional databases. These XML frequent itemsets lead researchers to find interesting XML patterns in large databases with the use of a threshold value. Apriori algorithm is one of the most leading solutions to discover XML frequent itemsets based on support value. XML frequent itemsets consist of similar items which show evidence of association. This relationship can be found with the use of Bayesian Network by learning structure of XML frequent itemsets. K2 algorithm is used to learn the structure of XML frequent itemsets. In this work, we propose a novel Apriori K2 algorithm. This algorithm is composed novel direction of apriori and K2 algorithms to find XML frequent itemsets and learning a level-wise Bayesian Network structure. For learning each level of this structure, XML frequent itemsets are found from XML candidate itemsets with the use of support measure using apriori algorithm. An updated binary table is prepared based on XML frequent itemset during the execution of apriori algorithm. K2 algorithm is used in conjunction with apriori algorithm to learn Bayesian Network structure of XML large frequent itemsets and find their relationship at each level. We have extensively tested our solution over UCI machine learning datasets and measured its performance. The results have shown that performance of our proposed solution is better than the combined performance of apriori and K2 algorithms.

Text detection in images is an emerging area of interest with a growing motivation to researchers. Various methodologies have been developed to localize text contained in scene images. One main application of localizing scene image text is to produce a real time support to visually impaired persons. To design a real-time support platform for visually impaired persons, classification of textual information, i.e. character and non-character information can provide a baseline for further research. However, the challenge exists in choosing the optimum classifier for this purpose. In this work, first, we used Maximally Stable Extremal Regions (MSERs) to detect character candidates in a scene image; then, we trained several classifiers, i.e., AdaboostM1, Bayesian Logistic Regression, Naïve Bayes, and Bayes Net, to classify MSERs as characters and non-characters; and finally, we compared and analyzed the performances of these classifiers empirically. From experiments, it has been concluded that Bayesian Logistic Regression provides the better accuracy over the other three classifiers. This work argues that MSER based character candidates extraction and Bayesian Logistic Regression based text classification are two prominent and potential techniques in scene text detection.

Although existing online tracking algorithms can solve the problems of scene illumination changes, partial or full object occlusions, and pose variation, there are still two weaknesses, inadequacy of training data and drift problem. Considering these, Compressive Tracking algorithm (CT) [1] extracts features from compressed domain, and classified object and background via a naive Bayes classier with online update. To further solve the problems of drift and inadequacy of training data, we introduce transfer learning into CT to take full advantage of prior information and propose a self-traininglike transfer learning algorithm. It selects training samples from samples collection to update classifier by the conduction of the classifier constructed at first frame. Eventually we introduce self-training-like transfer learning algorithm into CT to construct a novel tracking algorithm called Transfer Learning based Compressive Tracking (TLCT). Experimental results on 17 publicly available challenging sequences have shown the effectiveness and robustness of our algorithm.

We propose and implement a robust text detection system, which is a prominent step-in for text retrieval in natural scene images or videos. Our system includes several key components: (1) A fast and effective pruning algorithm is designed to extract Maximally Stable Extremal Regions as character candidates using the strategy of minimizing regularized variations. (2) Character candidates are grouped into text candidates by the single-link clustering algorithm, where distance weights and threshold of clustering are learned automatically by a novel self-training distance metric learning algorithm. (3) The posterior probabilities of text candidates corresponding to non-text are estimated with an character classifier; text candidates with high probabilities are then eliminated and finally texts are identified with a text classifier. The proposed system is evaluated on the ICDAR 2011 Robust Reading Competition dataset and a publicly available multilingual dataset; the f measures are over 76% and 74% which are significantly better than the state-of-the-art performances of 71% and 65%, respectively.

This paper investigates on entity conceptualization and its applications based on a Web-scale knowledge base. Firstly, inspired by the spreading activation theory from Cognitive Psychology, a model for entity conceptualization in a context-free setting is introduced. Secondly, an extended model with additional consideration on environmental factors is proposed for context-sensitive entity conceptualization. With entity conceptualization, we propose a method for generating concept-level knowledge based on conceptualization of large-scale factual triple form knowledge acquired from the Wiki-Encyclopedias. We provide several typicality measures for understanding concept-level knowledge in different perspectives. Finally, we discuss how the concept-level knowledge and its typicality measures can be used for finding incorrect knowledge contributed by Wiki-Encyclopedia contents contributors.

Physiological parameters of vertebrae are important for cervical condition assessment. In order to measure the parameters fast and accurately, automatic segmentation instead of manual key point placement has become an imperative for diagnosing. We propose an applicable automatic segmentation system for medical image of cervical spine. The system includes a series of algorithms: a parallel cascade structure based Haar-like features and the AdaBoost learning algorithm used to detect the location of cervical vertebrae as a initial position of Active Appearance Model (AAM), multi-resolution AAM search applied to improve the speed and accuracy of AAM fit, and combination of global AAM and local AAM used to achieve more effective matching of details of vertebrae. Experiments on the cervical spine databases show a significant increase in speed, robustness and quality of fit compared to previous methods.

Web-based collaborative knowledge bases collect human knowledge through the Web. They can be used for answering questions or support different knowledge intensive applications on the Web. From a statistical point of view, they usually reveal some interesting characteristics, which can be acquired through statistical analysis to get deeper understanding of these kinds of knowledge bases. In this paper, we build a semantic knowledge base using the triples extracted from a Chinese wiki Web site called Baidu Baike. We make an investigation on the statistical results on the building process and structural characteristics of this knowledge base. We explain what we have observed and inferred and how the conclusion can help to understand the process of building large scale Web based collaborative knowledge bases and how to make them better.

Document images captured by a mobile phone camera often have perspective distortions. In this paper, fast and robust vanishing point detection methods for such perspective documents are presented. Most of previous methods are either slow or unstable. Based on robust detection of text baselines and character tilt orientations, our proposed technology is fast and robust with the following features: (1) quick detection of vanishing point candidates by clustering and voting on the Gaussian sphere space, and (2) precise and efficient detection of the final vanishing points using a hybrid approach, which combines the results from clustering and projection analysis. The rectified image acceptance rate for Mobile Cam-based documents, signboards and posters is more than 98% with an average speed of about 100ms.

We describe a novel variational level set based method for delineating the sub thalamic nucleus (STN) region of human brains from magnetic resonance (MR) images. Based on the understanding of specific imaging characteristic of STN, we apply a narrow band limitation in the region-based energy function for localizing the STN from initial contour information provided by doctors. The validity of the algorithm was tested on practical Parkinson's Patients' T1-weighted magnetic resonance images. Comparing to traditional edge-based and global region-based level set methods, our method achieves more reliable segmentation results. Also, our method is insensitive to the initial contour placement, making it applicable in practical surgical navigations.

Today, more and more foreigners go to China and are emerged into studying Chinese. Thereinto, how to write Chinese characters is a very important and difficult task. As computers and internets develop, many teachers for Chinese Education want to use pattern recognition technologies to automatically evaluate and direct the quality of Chinese characters written by foreign students through document scanning. Actually, this is a handwritten character evaluation and identification problem. In this paper, we investigate and compare several character identification methods for Chinese Education within a classification framework. First, some two-class classification techniques with different features and classifiers (BP neural networks and SVMs) are investigated to identify each handwritten Chinese character. Moreover, in character identification, positive examples are always conjunctive, but negative examples are diffused in most cases. Consequently, we use one-class classification technique (one-class SVMs) to perform this handwritten character identification. In order to overcome the sensitivity to the SVM parameters, we propose a variant one-class SVM system - Bagged One-Class SVMs, which integrate many one-class SVMs with sample bagging. Some experiments of evaluating real handwritten Chinese characters by foreigners are performed, which show that general handwritten character identification is a big challenge and one-class classification technique is a potential researching and developing direction.

Topic relevance of pages and hyperlinks is the key issue in focused crawling. In this paper, an improved topic relevance algorithm for focused crawling is proposed. First, we implement a prototype system of the focused crawler - a topic-specific news gathering system which is prepared for comparative experiments on different similarity measures with the anchor text. Second, experiments on Chinese text corpus show that using LSI (Latent Semantic Indexing) outperforms using TF-IDF (term frequency- inverse document frequency) for hyperlink topic relevance prediction and pages topic relevance calculation. Third, in real crawling experiments on the prototype system, the crawler using TF-IDF has high performance with the accumulated topic relevance increasing quickly at the beginning of crawling, however the crawler using LSI can find more related pages and tunnel through. Fourth, combining their advantages of LSI and TF-IDF, we propose TFIDF+LSI algorithm to guide the crawling. Last, the crawler using TFIDF+LSI performs the same crawl task and demonstrates the combination advantage of TF-IDF and LSI. The experiment suggests that the crawler's performance using TFIDF+LSI is greatly superior to that using either TF-IDF or LSI respectively.

With rejection strategies in a handwriting recognition system, we are able to improve the reliability and accuracy of the recognized characters. In this paper, we propose several rejection strategies with multiple classifiers for handwritten character recognition. First, the rejection strategy for the single classifier is introduced, which is composed of three stages: initial scaling, confidence measure calculation, and rejection performing. Then, we analyze rejection strategies for multiple classifiers. We divided our rejection strategies into two categories: (1) for voting combination; and (2) for linear combination with multiple classifiers. In the voting combination style, three rejection strategies, OR, AND, and VOTING, are proposed. And for the linear combination one, rejection strategies for average and weighted combination are analyzed respectively. We also experiment and compare our rejection strategies with handwritten digit recognition.

For combining classifiers at measurement level, thediverse outputs of classifiers should be transformed touniform measures that represent the confidence ofdecision, hopefully, the class probability or likelihood.This paper presents our experimental results of classifiercombination using confidence evaluation. We test threetypes of confidences: log-likelihood, exponential andsigmoid. For re-scaling the classifier outputs, we usethree scaling functions based on global normalizationand Gaussian density estimation. Experimental results inhandwritten digit recognition show that via confidenceevaluation, superior classification performance can beobtained using simple combination rules.

Classifier subset selection (CSS) from a large ensemble isan effective way to design multiple classifier systems(MCSs). Given a validation dataset and a selectioncriterion, the task of CSS is reduced to searching thespace of classifier subsets to find the optimal subset. Thisstudy investigates the search efficiency of geneticalgorithm (GA) and sequential search methods for CSS.In experiments of handwritten digit recognition, we selecta subset from 32 candidate classifiers with aim to achievehigh accuracy of combination. The results show that inrespect of optimality, no method wins others in all cases.All the methods are very fast except the generalized plus land take away r(GPTA) method.