What is it about?
In the method, a discriminative loss function for the video category based on group sparse coding of sparse coefficients, is introduced into the structure of the locality-sensitive dictionary learning method. After solving the optimized dictionary, the sparse coefficients for the testing video feature samples are obtained, and then the classification result for video semantic is realized by minimizing the error between the original and reconstructed samples.
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Why is it important?
video semantic analysis based on this approach is very important because, it is able to capture fully the discriminative information inhibiting the locality structure of video semantic data containing, which contains more discriminative information essential for classification. Furthermore, similar coding outcomes are realized from video features with the same video category. The experiment results show that, the proposed GSLSDL approach significantly improves the performance of video semantic detection compared with the competing methods, and it is robust to various diverse environments of video.
Perspectives
The GSLSDL algorithm enables the adjustment of the dictionary adaptively, using the differences between the reconstructed dictionary atoms and the training samples with the locality adapter. More so, the implementation of the locality adaptor at both dictionary learning (DL) and sparse coding stages increases the efficiency of the algorithm due to the locality and similarity of the dictionary atoms. The dictionary information could also be fully exploited by the proposed algorithm with the introduction of a discriminant loss function during the DL stage, to obtain a more discriminative information and enhance the classification ability of spares representation features. The dictionaries learned could realistically represent video semantic more, and thus gives a better representation of the samples belonging to the sparse coefficients.
Dr. Ben-Bright Benuwa
Data Link Institute
Read the Original
This page is a summary of: Group sparse based locality – sensitive dictionary learning for video semantic analysis, Multimedia Tools and Applications, July 2018, Springer Science + Business Media,
DOI: 10.1007/s11042-018-6417-3.
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