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Many representation methods were built for gray image textures. However, they are not effective for color textures in general. To alleviate this problem, in this paper we propose a novel Compact Interchannel Sampling Difference Descriptor (CISDD) for color texture classification. In particular, considering sampling-based method can capture more directional information, we first use a heavy-tailed distribution, t-distribution to generate sample points in the image patch to calculate the micro-block difference. Then we model the interchannel relationship of color texture image by using dense micro-block differences. Furthermore, we utilize principal component analysis (PCA) to reduce the dimensions of the features encoded by the Fisher vector, and construct a Compact Interchannel Sampling Difference Descriptor (CISDD) for representing color texture image. Finally, experimental results on five published standard texture datasets (KTH-TIPS, VisTex, CUReT, USPTex and Colored Brodatz) reveal that CISDD is effective and outperforms thirteen representative color texture classification methods. [ABSTRACT FROM AUTHOR]

Preserving projection-based methods are good for finding the manifold structure embedded in data. As they use the Euclidean distance as a metric, which is sensitive to noise and outliers in data, nuclear norm-based 2D locality preserving projection (NN-2DLPP) is thus proposed to improve the robustness of 2DLPP. However, NN-2DLPP does not consider the discriminant ability of data. In order to improve the discriminant ability of preserving projection methods, in this paper, we use preserving projection learning with structurally incoherence of data and propose structurally incoherent low-rank 2DLPP (SILR-2DLPP) for image classification. This approach provides a discriminative representation of preserving projection learning by recovering the distinct different classes of the data. SILR-2DLPP searches the optimal subspace and low-rank representation simultaneously. We further extend SILR-2DLPP to a kernel case and propose kernel SILR-2DLPP (KSILR-2DLPP) to obtain a nonlinear representation. The theoretical analysis including the convergence and computational complexity of SILR-2DLPP are presented. To verify the performance of SILR-2DLPP and KSILR-2DLPP, six well-known image databases were used in the experiments. The experimental results show that the proposed methods are superior to the previous preserving projection methods for image classification. [ABSTRACT FROM AUTHOR]

Nonnegative matrix factorization (NMF), known as a famous matrix factorization technique, has been widely used in pattern recognition and computer vision. NMF represents the input data matrix as a product of two nonnegative factors. As NMF is based on the Euclidean distance, which is sensitive to noise or errors in the data, some robust NMF methods are proposed. Mainly focusing on parts-based representation, these robust NMF methods often neglect global representation of data. In fact, the global geometry information of data is more robust than the local information about the noisy data in terms of image classification. In order to effectively improve the robustness of NMF and learn part-based and global representation of the data, a novel method low-rank nonnegative factorization (LRNF) is proposed in this paper. First, we assume that the data are grossly corrupted, and the $L_{1} $ norm is used as a sparse constraint on the assumed noise matrix. Then, LRNF learns a low-rank matrix with the global representation ability. Finally, we make a nonnegative factorization of the learned low-rank matrix. We can obtain a base matrix, which preserves locality and globality properties of the data in the meantime. Extensive experiments have been conducted on nine real-world image databases to verify the performance of the proposed LRNF method by comparing with the state-of-the-art algorithms on robust dimensionality reduction. [ABSTRACT FROM AUTHOR]

Moving object detection is a key to intelligent video analysis. On the one hand, what moves are not only interesting objects but also noise and cluttered background. On the other hand, moving objects without rich texture are prone to not be detected. Therefore, there are undesirable false alarms and missed alarms in the results of many algorithms of moving object detection. To reduce the false alarms and missed alarms, in this paper we propose to incorporate a saliency map into an incremental subspace analysis framework in which the saliency map makes the estimated background have less of a chance than the foreground (i.e., moving objects) to contain salient objects. The proposed objective function systematically takes into account the properties of sparsity, low rank, connectivity, and saliency. An alternative minimization algorithm is proposed to seek the optimal solutions. The experimental results on both the Perception Test Images Sequences data set and Wallflower data set demonstrate that the proposed method is effective in reducing false alarms and missed alarms. [ABSTRACT FROM PUBLISHER]

Detecting moving objects from the video sequences has been treated as a challenging computer vision task, since the problems of dynamic background, multi-scale moving objects and various noise interference impact the corresponding feasibility and efficiency. In this paper, a novel spatio-temporal online matrix factorization (STOMF) method is proposed to detect multi-scale moving objects under dynamic background. To accommodate a wide range of the real noise distractions, we apply a specific mixture of exponential power (MoEP) distributions to the framework of low-rank matrix factorization (LRMF). For the optimization of solution algorithm, a temporal difference motion prior (TDMP) model is proposed, which estimates the motion matrix and calculates the weight matrix. Moreover, a partial spatial motion information (PSMI) post-processing method is further designed to implement multi-scale objects extraction in varieties of complex dynamic scenes, which utilizes partial background and motion information. The superiority of the STOMF method is validated by massive experiments on practical datasets, as compared with state-of-the-art moving objects detection approaches. [ABSTRACT FROM AUTHOR]

2-D linear discriminant analysis (2DLDA) has been widely used in pattern recognition and image classification. 2DLDA selects discriminative features from the up and left corner of images. However, 2DLDA uses the Frobenius norm (F-norm), which is sensitive to noise or outliers in data, as a metric. In this paper, we propose a novel framework, called horizontal and vertical nuclear norm-based 2DLDA (HVNN-2DLDA) for image representation. In the proposed framework, HVNN-2DLDA methods (i.e., HNN-2DLDA and VNN-2DLDA) are proposed, and both use the nuclear norm as a criterion. The nuclear norm can provide more structure and global information for the reconstruction of noisy images. HNN-2DLDA and VNN-2DLDA represent images in the row and column directions, respectively. In addition, by combining the row and column directions, we propose a bilateral nuclear norm-based 2DLDA method called BNN-2DLDA. The advantage of BNN-2DLDA over HNN-2DLDA and VNN-2DLDA is that an image sample can be represented by both the row and the column directions instead of only the row or column direction. HVNN-2DLDA learns a set of local optimal projection vectors by maximizing the ratio of the nuclear norm of the between-class scatter matrix and the nuclear norm of the within-class scatter matrix. To verify the robustness and recognition performance in image classification of HVNN-2DLDA, six public image databases are used for experiments. The experimental results demonstrate the effectiveness and the feasibility of the proposed framework. [ABSTRACT FROM AUTHOR]

Traffic videos are often recorded by vehicle-mounted cameras. Compared with videos recorded by handheld cameras, traffic videos suffer from more challenges, such as higher frequency and more violent jitters, dynamic scenes, large moving objects, and parallax, which can result in significant visual quality degradation. To address these challenges for traffic videos, we propose a special stabilization method. The key aspect of our method is a feedback strategy that divides the extracted feature trajectories into background trajectories and foreground trajectories by feeding back the previous trajectory classification results. The method can perform robustly, even in the case of large moving objects and parallax. Furthermore, our method maintains the number of available background trajectories within a reasonable range via two refinement strategies. One strategy attempts to reliably recover background trajectories from misjudged foreground trajectories when there are an insufficient number of background trajectories. The other strategy can adaptively adjust the number of feature points in each frame to efficiently avoid too many or too few background trajectories. With the obtained background trajectories, a homography matrix between each frame and its stabilized view is computed and implemented to warp the frame image to produce smooth videos. Experimental results confirm that our method is both effective in stabilizing traffic videos and quite robust against large moving objects and parallax. [ABSTRACT FROM PUBLISHER]

With the rapid development of the mobile service and online social networking service, a large number of mobile images are generated and shared on the social networks every day. The visual content of these images contains rich knowledge for many uses, such as social categorization and recommendation. Mobile image labeling has, therefore, been proposed to understand the visual content and received intensive attention in recent years. In this paper, we present a novel mobile image labeling scheme on the cloud, in which mobile images are first and efficiently transmitted to the cloud by Hamming compressed sensing, such that the heavy computation for image understanding is transferred to the cloud for quick response to the queries of the users. On the cloud, we design a sparse correntropy framework for robustly learning the semantic content of mobile images, based on which the relevant tags are assigned to the query images. The proposed framework (called maximum correntropy-based mobile image labeling) is very insensitive to the noise and the outliers, and is optimized by a half-quadratic optimization technique. We theoretically show that our image labeling approach is more robust than the squared loss, absolute loss, Cauchy loss, and many other robust loss function-based sparse coding methods. To further understand the proposed algorithm, we also derive its robustness and generalization error bounds. Finally, we conduct experiments on the PASCAL VOC’07 data set and empirically demonstrate the effectiveness of the proposed robust sparse coding method for mobile image labeling. [ABSTRACT FROM PUBLISHER]

Background information is crucial for many video surveillance applications such as object detection and scene understanding. In this paper, we present a novel pixel-to-model (P2M) paradigm for background modeling and restoration in surveillance scenes. In particular, the proposed approach models the background with a set of context features for each pixel, which are compressively sensed from local patches. We determine whether a pixel belongs to the background according to the minimum P2M distance, which measures the similarity between the pixel and its background model in the space of compressive local descriptors. The pixel feature descriptors of the background model are properly updated with respect to the minimum P2M distance. Meanwhile, the neighboring background model will be renewed according to the maximum P2M distance to handle ghost holes. The P2M distance plays an important role of background reliability in the 3-D spatial–temporal domain of surveillance videos, leading to the robust background model and recovered background videos. We applied the proposed P2M distance for foreground detection and background restoration on synthetic and real-world surveillance videos. Experimental results show that the proposed P2M approach outperforms the state-of-the-art approaches both in indoor and outdoor surveillance scenes. [ABSTRACT FROM PUBLISHER]