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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]

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]