With the rapid growth of network traffic, data has become increasingly complex and voluminous, posing significant challenges for accurate anomaly detection. Traditional deep learning approaches often fail to capture the rich interdependencies and heterogeneous nature of traffic features, limiting their effectiveness in identifying subtle or evolving abnormal patterns. To address these challenges, this paper proposes GS-HF (GraphSAGE with heterogeneous features), an anomaly detection framework. The method integrates both statistical and image-like features extracted from raw traffic data to capture multi-dimensional characteristics, and then constructs a graph based on the fused heterogeneous features to better represent relationships among traffic flows. An improved GraphSAGE model is applied for detection, incorporating Focal Loss to handle class imbalance in real-world anomaly datasets. Extensive experiments on multiple network traffic datasets demonstrate that GS-HF achieves superior detection performance compared to existing methods, highlighting its robustness and effectiveness in handling diverse and complex traffic patterns.

For the problem of straight-line cruise, a control method for the path following of marine robots based on direct dynamic programming of the desired yaw angle is proposed in this paper for underactuated marine robots. Different from the typical path following control method, i.e., the line of sight algorithm (LOS), the desired yaw angle of marine robots is dynamically programmed directly according to the lateral error on the desired path in the Serret-Frenet (SF) frame, Hence, the lateral error of the robot is guaranteed to converge to zero. Meanwhile, the longitudinal error of the robot is reduced by timely adjusting the virtual velocity on the tangent line of the desired path point. Furthermore, the control law is obtained based on the programmed desired yaw angle and prescribed longitudinal velocity, together with RBF neural network estimation. Finally, the control scheme proposed in this paper is used on a marine robot for simulation verification by comparing with an extent improved LOS path following control method. The simulation results demonstrate that the longitudinal and lateral following accuracy and the smoothness of the control signals have better performance when applying the proposed control scheme.

It is a significant challenge to classify images with multiple labels by using only a small number of labeled samples. One option is to learn a binary classifier for each label and use manifold regularization to improve the classification performance by exploring the underlying geometric structure of the data distribution. However, such an approach does not perform well in practice when images from multiple concepts are represented by high-dimensional visual features. Thus, manifold regularization is insufficient to control the model complexity. In this paper, we propose a manifold regularized multitask learning (MRMTL) algorithm. MRMTL learns a discriminative subspace shared by multiple classification tasks by exploiting the common structure of these tasks. It effectively controls the model complexity because different tasks limit one another's search volume, and the manifold regularization ensures that the functions in the shared hypothesis space are smooth along the data manifold. We conduct extensive experiments, on the PASCAL VOC'07 dataset with 20 classes and the MIR dataset with 38 classes, by comparing MRMTL with popular image classification algorithms. The results suggest that MRMTL is effective for image classification.

We propose an automatic approximation of the intrinsic manifold for general semi-supervised learning (SSL) problems. Unfortunately, it is not trivial to define an optimization function to obtain optimal hyperparameters. Usually, cross validation is applied, but it does not necessarily scale up. Other problems derive from the suboptimality incurred by discrete grid search and the overfitting. Therefore, we develop an ensemble manifold regularization (EMR) framework to approximate the intrinsic manifold by combining several initial guesses. Algorithmically, we designed EMR carefully so it 1) learns both the composite manifold and the semi-supervised learner jointly, 2) is fully automatic for learning the intrinsic manifold hyperparameters implicitly, 3) is conditionally optimal for intrinsic manifold approximation under a mild and reasonable assumption, and 4) is scalable for a large number of candidate manifold hyperparameters, from both time and space perspectives. Furthermore, we prove the convergence property of EMR to the deterministic matrix at rate root-n. Extensive experiments over both synthetic and real data sets demonstrate the effectiveness of the proposed framework.

With the explosive emergence of vertical search domains, applying the broad-based ranking model directly to different domains is no longer desirable due to domain differences, while building a unique ranking model for each domain is both laborious for labeling data and time consuming for training models. In this paper, we address these difficulties by proposing a regularization-based algorithm called ranking adaptation SVM (RA-SVM), through which we can adapt an existing ranking model to a new domain, so that the amount of labeled data and the training cost is reduced while the performance is still guaranteed. Our algorithm only requires the prediction from the existing ranking models, rather than their internal representations or the data from auxiliary domains. In addition, we assume that documents similar in the domain-specific feature space should have consistent rankings, and add some constraints to control the margin and slack variables of RA-SVM adaptively. Finally, ranking adaptability measurement is proposed to quantitatively estimate if an existing ranking model can be adapted to a new domain. Experiments performed over Letor and two large scale data sets crawled from a commercial search engine demonstrate the applicabilities of the proposed ranking adaptation algorithms and the ranking adaptability measurement.

Existing video concept detectors are generally built upon the kernel based machine learning techniques, e.g., support vector machines, regularized least squares, and logistic regression, just to name a few. However, in order to build robust detectors, the learning process suffers from the scalability issues including the high-dimensional multi-modality visual features and the large-scale keyframe examples. In this paper, we propose parallel lasso (Plasso) by introducing the parallel distributed computation to significantly improve the scalability of lasso (thel1regularized least squares). We apply the parallel incomplete Cholesky factorization to approximate the covariance statistics in the preprocess step, and the parallel primal-dual interior-point method with the Sherman-Morrison-Woodbury formula to optimize the model parameters. For a dataset withnsamples in ad-dimensional space, compared with lasso, Plasso significantly reduces complexities from the originalO(d3) for computational time andO(d2) for storage space toO(h2d/m) andO(hd/m) , respectively, if the system hasmprocessors and the reduced dimensionhis much smaller than the original dimensiond. Furthermore, we develop the kernel extension of the proposed linear algorithm with the sample reweighting schema, and we can achieve similar time and space complexity improvements [time complexity fromO(n3) toO(h2n/m) and the space complexity fromO(n2) toO(hn/m), for a dataset withntraining examples]. Experimental results on TRECVID video concept detection challenges suggest that the proposed method can obtain significant time and space savings for training effective detectors with limited communication overhead.

Existing image retrieval systems suffer from a performance variance for different queries. Severe performance variance may greatly degrade the effectiveness of the subsequent query-dependent ranking optimization algorithms, especially those that utilize the information mined from the initial search results. In this paper, we tackle this problem by proposing a query difficulty guided image retrieval system, which can predict the queries' ranking performance in terms of their difficulties and adaptively apply ranking optimization approaches. We estimate the query difficulty by comprehensively exploring the information residing in the query image, the retrieval results, and the target database. To handle the high-dimensional and multi-model image features in the large-scale image retrieval setting, we propose a linear multiple feature embedding algorithm which learns a linear transformation from a small set of data by integrating a joint subspace in which the neighborhood information is preserved. The transformation can be effectively and efficiently used to infer the subspace features of the newly observed data in the online setting. We prove the significance of query difficulty to image retrieval by applying it to guide the conduction of three retrieval refinement applications, i.e., reranking, federated search, and query suggestion. Thorough empirical studies on three datasets suggest the effectiveness and scalability of the proposed image query difficulty estimation algorithm, as well as the promising of the image difficulty guided retrieval system.

Content-based web image retrieval based on the query-by-example (QBE) principle remains a challenging problem due to the semantic gap as well as the gap between a user's intent and the representativeness of a typical image query. In this article, we propose to address this problem by integrating query-related contextual information into an advanced query model to improve the performance of QBE-based web image retrieval. We consider both the local and global context of the query image. The local context can be inferred from the web pages and the click-through log associated with the query image, while the global context is derived from the entire corpus comprising all web images and the associated web pages. To effectively incorporate the local query context we propose a language modeling based approach to deal with the combined structured query representation from the contextual and visual information. The global query context is integrated by the multi-modal relevance model to “reconstruct” the query from the document models indexed in the corpus. In this way, the global query context is employed to address the noise or missing information in the query and its local context, so that a comprehensive and robust query model can be obtained. We evaluated the proposed approach on a representative product image dataset collected from the web and demonstrated that the inclusion of the local and global query contexts significantly improves the performance of QBE-based web image retrieval.

With the rapid development of bag-of-visual-word model and its wide-spread applications in various computer vision problems such as visual recognition, image retrieval tasks, etc., fast visual word assignment becomes increasingly important, especially for some on-line services and large scale settings. The conventional approximate nearest neighbor mapping techniques purely consider the distribution of image local descriptors in the visual feature space and perform the mapping process independently for each descriptor. In this paper, we propose to involve the spatial correlation information to boost the efficiency of feature quantization. The visual words that frequently co-occur in the same local region of a large number of images are considered as spatial neighborhoods, which can be leveraged to boost the approximate mapping of neighbored local descriptors. Experimental results on a well-known image retrieval dataset demonstrate that, the proposed method is capable of improving the efficiency and precision of visual word assignment.

Existing web image search engines are mainly designed to optimize topical relevance. However, according to our user study, attractiveness is becoming a more and more important factor for web image search engines to satisfy users' search intentions. Important as it can be, web image attractiveness from the search users' perspective has not been sufficiently recognized in both the industry and the academia. In this paper, we present a definition of web image attractiveness with three levels according to the end users' feedback, including perceptual quality, aesthetic sensitivity and affective tune. Corresponding to each level of the definition, various visual features are investigated on their applicability to attractiveness estimation of web images. To further deal with the unreliability of visual features induced by the large variations of web images, we propose a contextual approach to integrate the visual features with contextual cues mined from image EXIF information and the associated web pages. We explore the role of attractiveness by applying it to various stages of a web image search engine, including the online ranking and the interactive reranking, as well as the offline index selection. Experimental results on three large-scale web image search datasets demonstrate that the incorporation of attractiveness can bring more satisfaction to 80% of the users for ranking/reranking search results and 30.5% index coverage improvement for index selection, compared to the conventional relevance based approaches.

The well-known bag-of-features (BoF) model is widely utilized for large scale image retrieval. However, BoF model lacks the spatial information of visual words, which is informative for local features to build up meaningful visual patches. To compensate for the spatial information loss, in this paper, we propose a novel query expansion method called Spatial Co-occurrence Query Expansion (SCQE), by utilizing the spatial co-occurrence information of visual words mined from the database images to boost the retrieval performance. In offline phase, for each visual word in the vocabulary, we treat the visual words that are frequently co-occurred with it in the database images as neighbors, base on which a spatial co-occurrence graph is built. In online phase, a query image can be expanded with some spatial co-occurred but unseen visual words according to the spatial co-occurrence graph, and the retrieval performance can be improved by expanding these visual words appropriately. Experimental results demonstrate that, SCQE achieves promising improvements over the typical BoF baseline on two datasets comprising 5K and 505K images respectively.

Existing image retrieval systems suffer from a radical performance variance for different queries. The bad initial search results for "difficult" queries may greatly degrade the performance of their subsequent refinements, especially the refinement that utilizes the information mined from the search results, e.g., pseudo relevance feedback based reranking. In this paper, we tackle this problem by proposing a query difficulty guided image retrieval system, which selectively performs reranking according to the estimated query difficulty. To improve the performance of both reranking and difficulty estimation, we apply multiview embedding (ME) to images represented by multiple different features for integrating a joint subspace by preserving the neighborhood information in each feature space. However, existing ME approaches suffer from both "out of sample" and huge computational cost problems, and cannot be applied to online reranking or offline large-scale data processing for practical image retrieval systems. Therefore, we propose a linear multiview embedding algorithm which learns a linear transformation from a small set of data and can effectively infer the subspace features of new data. Empirical evaluations on both Oxford and 500K ImageNet datasets suggest the effectiveness of the proposed difficulty guided retrieval system with LME.

Multi-task learning (MTL) plays an important role in image analysis applications, e.g. image classification, face recognition and image annotation. That is because MTL can estimate the latent shared subspace to represent the common features given a set of images from different tasks. However, the geometry of the data probability distribution is always supported on an intrinsic image sub-manifold that is embedded in a high dimensional Euclidean space. Therefore, it is improper to directly apply MTL to multiclass image classification. In this paper, we propose a manifold regularized MTL (MRMTL) algorithm to discover the latent shared subspace by treating the high-dimensional image space as a sub-manifold embedded in an ambient space. We conduct experiments on the PASCAL VOC'07 dataset with 20 classes and the MIR dataset with 38 classes by comparing MRMTL with conventional MTL and several representative image classification algorithms. The results suggest that MRMTL can properly extract the common features for image representation and thus improve the generalization performance of the image classification models.

The state-of-the-art metric-learning algorithms cannot perform well for domain adaptation settings, such as cross-domain face recognition, image annotation, etc., because labeled data in the source domain and unlabeled ones in the target domain are drawn from different, but related distributions. In this paper, we propose the domain adaptation metric learning (DAML), by introducing a data-dependent regularization to the conventional metric learning in the reproducing kernel Hilbert space (RKHS). This data-dependent regularization resolves the distribution difference by minimizing the empirical maximum mean discrepancy between source and target domain data in RKHS. Theoretically, by using the empirical Rademacher complexity, we prove risk bounds for the nearest neighbor classifier that uses the metric learned by DAML. Practically, learning the metric in RKHS does not scale up well. Fortunately, we can prove that learning DAML in RKHS is equivalent to learning DAML in the space spanned by principal components of the kernel principle component analysis (KPCA). Thus, we can apply KPCA to select most important principal components to significantly reduce the time cost of DAML. We perform extensive experiments over four well-known face recognition datasets and a large-scale Web image annotation dataset for the cross-domain face recognition and image annotation tasks under various settings, and the results demonstrate the effectiveness of DAML.

Object retrieval aims at retrieving images containing objects similar to the query object captured in the region of interest (ROI) of the query image. Boosted by the invention and wide popularity of SIFT image features and bag-of-visual-words image representation, object retrieval has progressed significantly in the past years and has already found deployment in real-life applications and products. While existing object retrieval methods perform well in many cases, they may fail to return satisfactory results if the ROI specified by the user is inaccurate or if the object captured there is too small to be represented using discriminative features and consequently to be matched with similar objects in the image collection. In order to improve the object retrieval performance also in these difficult cases, we propose in this paper an object retrieval method that exploits the information about the visual context of the query object and employ it to compensate for possible uncertainty in feature-based query object representation. Contextual information is drawn from the visual elements surrounding the query object in the query image. We consider the ROI as an uncertain observation of the latent search intent and the saliency map detected for the query image as a prior. Then a language modeling approach is employed to devise a contextual object retrieval (COR) model. There, the relevance score is determined based on the search intent scores that are inferred from the uncertain ROI and the saliency prior. The usefulness of the contextual information for object retrieval and the effectiveness of the proposed COR model are demonstrated and evaluated on three representative image datasets.

The ranking models of existing image/video search engines are generally based on associated text while the visual content is actually neglected. Imperfect search results frequently appear due to the mismatch between the textual features and the actual visual content. Visual reranking, in which visual information is applied to refine text based search results, has been proven to be effective. However, the improvement brought by visual reranking is limited, and the main reason is that the errors in the text-based results will propagate to the refinement stage. In this paper, we propose a Content-Aware Ranking model based on “learning to rank” framework, in which textual and visual information are simultaneously leveraged in the ranking learning process. We formulate the Content-Aware Ranking based on large margin structured output learning, by modeling the visual information into a regularization term. The direct optimization of the learning problem is nearly infeasible since the number of constraints is huge. The efficient cutting plane algorithm is adopted to learn the model by iteratively adding the most violated constraints. Extensive experimental results on a large-scale dataset collected from a commercial Web image search engine, as well as the TRECVID 2007 video search dataset, demonstrate that the proposed ranking model significantly outperforms the state-of-the-art ranking and reranking methods.

The regularization principals [31] lead approximation schemes to deal with various learning problems, e.g., the regularization of the norm in a reproducing kernel Hilbert space for the ill-posed problem. In this paper, we present a family of subspace learning algorithms based on a new form of regularization, which transfers the knowledge gained in training samples to testing samples. In particular, the new regularization minimizes the Bregman divergence between the distribution of training samples and that of testing samples in the selected subspace, so it boosts the performance when training and testing samples are not independent and identically distributed. To test the effectiveness of the proposed regularization, we introduce it to popular subspace learning algorithms, e.g., principal components analysis (PCA) for cross-domain face modeling; and Fisher's linear discriminant analysis (FLDA), locality preserving projections (LPP), marginal Fisher's analysis (MFA), and discriminative locality alignment (DLA) for cross-domain face recognition and text categorization. Finally, we present experimental evidence on both face image data sets and text data sets, suggesting that the proposed Bregman divergence-based regularization is effective to deal with cross-domain learning problems.

Recently, various domain-specific search engines emerge, which are restricted to specific topicalities or document formats, and vertical to the broad-based search. Simply applying the ranking model trained for the broad-based search to the verticals cannot achieve a sound performance due to the domain differences, while building different ranking models for each domain is both laborious for labeling sufficient training samples and time-consuming or the training process. In this paper, to address the above difficulties, we investigate two problems: (1) whether we can adapt the ranking model learned for existing Web page search or verticals, to the new domain, so that the amount of labeled data and the training cost is reduced, while the performance requirement is still satisfied; and (2) how to adapt the ranking model from auxiliary domains to a new target domain. We address the second problem from the regularization framework and an algorithm called ranking adaptation SVM is proposed. Our algorithm is flexible enough, which needs only the prediction from the existing ranking model, rather than the internal representation of the model or the data from auxiliary domains. The first problem is addressed by the proposed ranking adaptability measurement, which quantitatively estimates if an existing ranking model can be adapted to the new domain. Extensive experiments are performed over Letor benchmark dataset and two large scale datasets crawled from different domains through a commercial internet search engine, where the ranking model learned for one domain will be adapted to the other. The results demonstrate the applicabilities of the proposed ranking model adaptation algorithm and the ranking adaptability measurement.

We propose an automatic approximation of the intrinsic manifold for general semi-supervised learning problems. Unfortunately, it is not trivial to define an optimization function to obtain optimal hyperparameters. Usually, pure cross-validation is considered but it does not necessarily scale up. A second problem derives from the suboptimality incurred by discrete grid search and overfitting problems. As a consequence, we developed an ensemble manifold regularization (EMR) framework to approximate the intrinsic manifold by combining several initial guesses. Algorithmically, we designed EMR very carefully so that it (a) learns both the composite manifold and the semi-supervised classifier jointly; (b) is fully automatic for learning the intrinsic manifold hyperparameters implicitly; (c) is conditionally optimal for intrinsic manifold approximation under a mild and reasonable assumption; and (d) is scalable for a large number of candidate manifold hyperparameters, from both time and space perspectives. Extensive experiments over both synthetic and real datasets show the effectiveness of the proposed framework.

Learning to rank has become a popular approach to build a ranking model for Web search recently. Based on our observation, the constitution of the training set will greatly influence the performance of the learned ranking model. Meanwhile, the number of queries in Web search is nearly infinite and the human labeling cost is expensive, hence a subset of queries need to be carefully selected for training. In this paper, we develop a greedy algorithm to sample the queries, by simultaneously taking the query density, difficulty and diversity into consideration. The experimental results on a collected Web search dataset comprising 2024 queries show that the proposed method can lead to a more informative training set for building an effective model.

In transductive active learning, after selecting the samples for labeling using existing sample selection strategy such as close-to-boundary, the constructed labeled set will be under a different distribution from the unlabeled set, which violates the i.i.d assumption of existing classifier. In this paper, by explicitly considering the distribution difference, we propose an algorithm called unbiased active learning. In such algorithm, the distribution difference, so-called sample selection bias, is not only considered into the classifier, but also incorporated into the sample selection process for introducing a better sample selection strategy. We apply the proposed method to image retrieval and the experimental results show that our unbiased active learning algorithm outperforms existing approaches.

In this paper we present a novel framework for semantic retrieval of video database. Each frame of video clips, characterized by its HSV (hue-saturation-value) color feature, is first projected onto the spatial principle components via CCIPCA (candid covariance-free incremental principal component analysis). Temporal Chebyshev polynomials for video clips of various lengths are captured subsequently. The similarity of two video clips is finally presented in a reasonable and computable form. The framework works incrementally and is suitable for videos of data streams in sequential order. Extensive experiments demonstrate that the framework can obtain promising results on video similarity comparison, and also with a comparably computational speedup.