Traditional recrawling methods learn navigation patterns in order to crawl related web pages. However, they cannot remove the redundancy found on the web, especially at the object level. To deal with this problem, we propose a new hypertext resource discovery method, called ``selective recrawling'' for object-level vertical search applications. The goal of selective recrawling is to automatically generate URL patterns, then select those pages that have the widest coverage, and least irrelevance and redundancy relative to a pre-defined vertical domain. This method only requires a few seed objects and can select the set of URL patterns that covers the greatest number of objects. The selected set can continue to be used for some time to recrawl web pages and can be renewed periodically. This leads to significant savings in hardware and network resources.

In this paper we propose a novel recrawling method based on navigation patterns called Selective Recrawling. The goal of selective recrawling is to automatically select page collections that have large coverage and little redundancy to a pre-defined vertical domain. It only requires several seed objects and can select a set of URL patterns to cover most objects. The selected set can be used to recrawl the web pages for quite a period of time and renewed periodically. Experiments on local event data show that our method can greatly reduce the downloading of web pages while keep the comparative object coverage.

We propose face recognition method with info-margin maximization (InfoMargin) from information theoretic viewpoint. It aims to achieve a low generalization error by maximizing the information divergence between the distributions of different classes while minimizing the entropy of the distribution in each single class. Experimental results show that our method outperforms the traditional face recognition methods.

In real sequence labeling tasks, statistics of many higher order features are not sufficient due to the training data sparseness, very few of them are useful. We describe Sparse Higher Order Conditional Random Fields (SHO-CRFs), which are able to handle local features and sparse higher order features together using a novel tractable exact inference algorithm. Our main insight is that states and transitions with same potential functions can be grouped together, and inference is performed on the grouped states and transitions. Though the complexity is not polynomial, SHO-CRFs are still efficient in practice because of the feature sparseness. Experimental results on optical character recognition and Chinese organization name recognition show that with the same higher order feature set, SHO-CRFs significantly outperform previous approaches.

This paper presents a novel work on the task of extracting data from Web forums. Millions of users contribute rich information to Web forum everyday, which has become an important resource for manyWeb applications, such as product opinion retrieval, social network analysis, and so on. The novelty of the proposed algorithm is that it can not only extract the pure text but also distinguish between the original post and replies. Experimental results on a large number of real Web forums indicate that the proposed algorithm can correctly ex

As an important form of complex questions, definition question attracts much attention from QA researchers. For many of the definition question answering systems, it is a core step to rank the candidate answer sentences, so that the top-k in the ranked list can be extracted. We integrate these evidences as features into a whole framework, and propose a novel method to learning weights of these features to rank the candidate answer sentences.

Linear Discriminant Analysis (LDA) is a popular feature extraction technique in statistical pattern recognition. However, it often suffers from the small sample size problem when dealing with high-dimensional data. Moreover, while LDA is guaranteed to find the best directions when each class has a Gaussian density with a common covariance matrix, it can fail if the class densities are more general. In this paper, a novel nonparametric linear feature extraction method, nearest neighbor discriminant analysis (NNDA), is proposed from the view of the nearest neighbor classification. NNDA finds the important discriminant directions without assuming the class densities belong to any particular parametric family. It does not depend on the nonsingularity of the within-class scatter matrix either. Then we give an approximate approach to optimize NNDA and an extension to k-NN. We apply NNDA to the simulated data and real world data, the results demonstrate that NNDA outperforms the existing variant LDA methods.

Linear discriminant analysis (LDA) is a popular feature extraction technique in face recognition. However, it often suffers from the small sample size problem when dealing with the high dimensional data. Moreover, while LDA is guaranteed to find the best directions when each class has a Gaussian density with a common covariance matrix, it can fail if the class densities are more general. In this paper; a new nonparametric linear feature extraction method, stepwise nonparametric margin maximum criterion (SNMMC), is proposed to find the most discriminant directions, which does not assume that the class densities belong to any particular parametric family and does not depend on the non- singularity of the within-class scatter matrix neither. On three datasets from ATT and FERET face databases, our experimental results demonstrate that SNMMC outperforms other methods and is robust to variations of pose, illumination and expression.

Linear discriminant analysis (LDA) is a popular feature extraction technique in statistical pattern recognition. However, it often suffers from the small sample size problem when dealing with the high dimensional image data. Moreover, while LDA is guaranteed to find the best directions when each class has a Gaussian density with a common covariance matrix, it can fail if the class densities are more general. In this paper, a new feature extraction method, nonparametric maximum margin criterion (NMMC), is proposed. NMMC finds the important discriminant directions without assuming the class densities belong to any particular parametric family, and it does not depend on the nonsingularity of the within-class scatter matrix. Our experimental results on the ATT and FERET face databases demonstrate that NMMC outperforms the existing variant LDA methods and the other state-of-art face recognition approaches.

Linear discriminant analysis (LDA) is popular feature extraction technique for face recognition. However, it often suffers the small sample size problem when dealing with the high dimensional face data. Moreover, the within-class and between-class scatter matrix used in LDA have low effective when dealing with face data of non-Gaussian density. In this paper, we propose a new method for face recognition. We first calculate the weighted dual personal subspaces to replace the within and between class matrix, then null space-based LDA is performed. The experiments show our method outperforms existing LDA and state-of-art face recognition approaches.

Videos are made up of shots, which are connected by variant shot transitions can be categorized into hard cuts and gradual transitions. And gradual transitions can be further classified into dissolve, fade in, fade out, wipe. Shot boundary detection is the first step for video content analysis, indexing and classification. In this paper, we propose a novel approach for gradual transition detection. Our schema is to estimate the peaks on the frame to frame difference curve by EM (Expectation Maximum) curve fitting. Each peak contour is approximated by a mixture of Gaussian and uniform distributions. The weight of uniform component, the average height and the relative height of the peak are used as input features for the decision tree classifier to discriminate gradual transitions from cuts and miscellaneous. Finally, we present a framework for shot boundary detection involving camera motion detection and the combination of cut and gradual transition detection results. The advantage of our method is that it can detect all kinds of gradual transition types, such as dissolve, wipe or special effects, due to the flexibility of EM curve fitting.

Advances in the media and entertainment industries, including streaming audio and digital TV, present new challenges for managing and accessing large audio-visual collections. Current content management systems support retrieval using low-level features, such as motion, color, and texture. However, low-level features often have little meaning for naive users, who much prefer to identify content using high-level semantics or concepts. This creates a gap between systems and their users that must be bridged for these systems to be used effectively. To this end, in this paper, we first present a knowledge-based video indexing and content management framework for domain specific videos (using basketball video as an example). We will provide a solution to explore video knowledge by mining associations from video data. The explicit definitions and evaluation measures (e.g., temporal support and confidence) for video associations are proposed by integrating the distinct feature of video data. Our approach uses video processing techniques to find visual and audio cues (e.g., court field, camera motion activities, and applause), introduces multilevel sequential association mining to explore associations among the audio and visual cues, classifies the associations by assigning each of them with a class label, and uses their appearances in the video to construct video indices. Our experimental results demonstrate the performance of the proposed approach.

Hierarchical video browsing and feature-based video retrieval are two standard methods for accessing video content. Very little research, however, has addressed the benefits of integrating these two methods for more effective and efficient video content access. In this paper, we introduce InsightVideo, a video analysis and retrieval system, which joins video content hierarchy, hierarchical browsing and retrieval for efficient video access. We propose several video processing techniques to organize the content hierarchy of the video. We first apply a camera motion classification and key-frame extraction strategy that operates in the compressed domain to extract video features. Then, shot grouping, scene detection and pairwise scene clustering strategies are applied to construct the video content hierarchy. We introduce a video similarity evaluation scheme at different levels (key-frame, shot, group, scene, and video.) By integrating the video content hierarchy and the video similarity evaluation scheme, hierarchical video browsing and retrieval are seamlessly integrated for efficient content access. We construct a progressive video retrieval scheme to refine user queries through the interactions of browsing and retrieval. Experimental results and comparisons of camera motion classification, key-frame extraction, scene detection, and video retrieval are presented to validate the effectiveness and efficiency of the proposed algorithms and the performance of the system.

We propose a patch-based approach for detecting natural objects on keyframes of video shots. We apply it on the extraction of semantic feature "vegetation" and "animal", and on some search tasks in TRECVID2003. Our detection method is based on color and texture features, and considers the spatial information as well. TRECVID evaluation shows that our approach works effectively and can deal with the special situation when the target object only occupies a small portion of the whole image. The main contribution of our approach is as follows: first, we devise a novel color weighting scheme which is named CF*IRF. Second, we use a patch-based detection method for the feature extraction task, and test it in an open large video corpus. Finally, spatial constraints of patches are defined in image tessellation, which provides more flexibility.

Image classification is very active and promising research domain in image retrieval and management. We propose a boosting image classification scheme with automatic selection of discriminative features. Firstly, we present an image feature called the orientational color correlogram (OCC) and apply it to image classification. OCC extends the color correlogram by adding in orientational information which can take into account both the local color correlation and the global context structure of an image. Secondly, we give a solution to feature selection for the very high dimensionality of OCC by using a boosting classification scheme which can select the most discriminative features automatically. In our experiments, only a small number of elements of OCC are selected, which can reduce the storage space of classifier models and speed up the classification process. The experimental results suggest the proposed method has preferable performances.

Key frame extraction is one of the basic procedures relating to video retrieval and summary. Efficient key frame extraction techniques will facilitate video browsing systems, which have wide applications in the real world. We establish a new criterion for well-representative key frames and correspondingly, create a key frame selection algorithm based on FF-ISF. Its novelty lies in that it not only uses intra-shot information but inter-shot information as well, for key frame extraction.

Recent advances in digital video compression and networks have made video more accessible than ever. However, the existing content-based video retrieval systems still suffer from the following problems. 1) Semantics-sensitive video classification problem because of the semantic gap between low-level visual features and high-level semantic visual concepts; 2) Integrated video access problem because of the lack of efficient video database indexing, automatic video annotation, and concept-oriented summary organization techniques. In this paper, we have proposed a novel framework, called ClassView, to make some advances toward more efficient video database indexing and access. 1) A hierarchical semantics-sensitive video classifier is proposed to shorten the semantic gap. The hierarchical tree structure of the semantics-sensitive video classifier is derived from the domain-dependent concept hierarchy of video contents in a database. Relevance analysis is used for selecting the discriminating visual features with suitable importances. The Expectation-Maximization (EM) algorithm is also used to determine the classification rule for each visual concept node in the classifier. 2) A hierarchical video database indexing and summary presentation technique is proposed to support more effective video access over a large-scale database. The hierarchical tree structure of our video database indexing scheme is determined by the domain-dependent concept hierarchy which is also used for video classification. The presentation of visual summary is also integrated with the inherent hierarchical video database indexing tree structure. Integrating video access with efficient database indexing tree structure has provided great opportunity for supporting more powerful video search engines.

Audio classification has been investigated for several years. It is one of the key components in audio and video applications. In prior work, the accuracy under complicated condition is not satisfactory enough and the results highly depend on the dataset. In this paper, we present a novel audio classification method based on maximum entropy model. By applying this method on some widely used features, different feature combinations are considered during model training and a better performance can be achieved. When evaluated it in TREC 2002 Video Track's speech/music feature extraction task, this method works well for both speech and music among participated systems.

Semantic segmentation is critical in video content analysis and retrieval. We propose an approach to semantic video segmentation by integrating multiple video cues into a generic scheme. We define a consistency measurement of contexts, continuity, which has two components, called cohesion and disjunction. As a result we can treat all the features, both low level features and semantic features, in the same way. At each shot boundary, the continuity measurement gives a confidence of being the boundary between two semantic units. The effectiveness of our approach has been demonstrated by experiments on all kinds of live videos.

Textual information in a video is very useful for video indexing and retrieving. Detecting text blocks in video frames is the first important procedure for extracting the textual information. Automatic text location is a very difficult problem due to the large variety of character styles and the complex backgrounds. In this paper, we describe the various steps of the proposed text detection algorithm. First, the gray scale edges are detected and smoothed horizontally. Second, the edge image is binarized, and run length analysis is applied to find candidate text blocks. Finally, each detected block is verified by an improved logical level technique (ILLT). Experiments show this method is not sensitive to color/texture changes of the characters, and can be used to detect text lines in news videos effectively.

Automatic image segmentation and object extraction play an important role in supporting content-based image coding, indexing, and retrieval. However, the low-level visual homogeneity critical (like color, texture, intensity, and so on) for segmentation do not lead to semantic objects directly because a semantic object can contain totally different gray levels, color, or texture. We propose an automatic model-based semantic object extraction algorithm by integrating object seeds with their region constraint graphs (perceptual models). Images are first partitioned into a set of homogeneous regions with accurate boundaries by integrating the results obtained by similarity-based region growing and edge detection procedures. We propose a 1-D fast entropic thresholding technique for determining the thresholds used in region growing and edge detection automatically. The object seeds, which are the intuitive and representative parts of semantic objects, are then distinguished from these homogeneous image regions. A seeded region aggregation procedure is used for merging the adjacent regions of a detected object seed to give a semantic object according to the perceptual model of the object. We focus on semantic human object generation by taking faces as object seeds and using a ratio-based perceptual model.

Two scaling theorems are given. Instead of delta functions, polynomial functions are used as input. The advantages are twofold: the smoothness conditions on kernels are not required, so that kernels of the form e/sup -k mod X mod / can be included; the proofs are based on calculus completely and so can be more easily understood.>

Fingerprint theorems, i.e. under what conditions the primal sketch can determine the image uniquely, are discussed. The weakness of A.L. Yuille and T. Poggio's fingerprint theorem (1986) is pointed out and two novel 1-D fingerprint theorems are presented. Then a practical algorithm based on one of these theorems is given for reconstructing the image from its primal sketch. From the given examples, it is shown that the fingerprint theorems are a substantial improvement over Yuille and Poggio's conjecture that Gaussian function is the only filter which can be used as the basis of a fingerprint theorem. The 1-D fingerprint theorems are generalized to 2-D ones.>

The Laplacian of Gaussian operator, Del /sup 2/G, is very important as an edge detector in the theory of computer vision. The bias of zero-crossing and output signal-to-noise-ratio of Del /sup 2/G under the models of four typical kinds of edges corrupted by white noise are given, and these theoretical results are confirmed by experiments. The relations among bias of zero-crossing, output and input signal-to-noise-ratio and parameter sigma of Del /sup 2/G are presented.>