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Video semantic detection has been one research hotspot in the field of human-computer interaction. In video features-oriented sparse representation, the features from the same category video could not achieve similar coding results. To address this, the Locality-Sensitive Discriminant Sparse Representation (LSDSR) is developed, in order that the video samples belonging to the same video category are encoded as similar sparse codes which make them have better category discrimination. In the LSDSR, a discriminative loss function based on sparse coefficients is imposed on the locality-sensitive sparse representation, which makes the optimized dictionary for sparse representation be discriminative. The LSDSR for video features enhances the power of semantic discrimination to optimize the dictionary and build the better discriminant sparse model. More so, to further improve the accuracy of video semantic detection after sparse representation, a weighted K-Nearest Neighbor (KNN) classification method with the loss function that integrates reconstruction error and discrimination for the sparse representation is adopted to detect video semantic concepts. The proposed methods are evaluated on the related video databases in comparison with existing sparse representation methods. The experimental results show that the proposed methods significantly enhance the power of discrimination of video features, and consequently improve the accuracy of video semantic concept detection. [ABSTRACT FROM AUTHOR]

Because of the Student- t distribution owning heavier tailed than the Gaussian distribution, under a Bayesian framework, a spatially variant finite mixture model with Student’s t -distribution component function is proposed for grayscale image segmentation. To avoid additional computational step and improve the efficiency of the proposed model, a representation of contextual mixing proportion is adopted. Secondly, the spatial information of the pixels is closely related to the Gaussian distribution of their neighborhood system. Thirdly, the inherent relationship between the Gaussian distribution and the Student’s t -distribution is adopted to optimize the unknown parameters of the proposed model, which simplifies the inference process and makes the proposed model to be easily implemented. Comprehensive experiments on synthetic noise images, simulated medical images and real-world grayscale images are presented to illustrate the superior performance of the proposed model in terms of the visual and quantitative comparison. [ABSTRACT FROM AUTHOR]