A video semantic detection method based on locality-sensitive discriminant sparse representation and weighted KNN |
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| Affiliation: | 1. School of Information Engineering, Guangdong University of Technology, PR China;2. Fujian Provincial Key Laboratory of Data Mining and Applications, Fujian University of Technology, Fujian, PR China;1. The State Key Laboratory of Fluid Power and Mechatronic Systems, Zhejiang University, No. 38 Zheda Road, Hangzhou, Zhejiang 310027, PR China;2. The Institute of Spacecraft System Engineering, No. 104 Youyi Road, Haidian, Beijing 100094, PR China;1. Department of Computer Science, Jinan University, Guangzhou, China;2. Nanjing University of Information Science & Technology, Nanjing, China;3. School of Data and Computer Science, Sun Yat-sen University, Guangzhou, China;4. Guangdong Provincial Big Data Collaborative Innovation Center, Shenzhen University, Shenzhen, China;1. College of Computer and Information Engineering, Xiamen University of Technology, Xiamen 361024, China;2. Department of Information Management, Chaoyang University of Technology, Taichung, Taiwan;1. School of Electronic and Information Engineering, Xi’an Jiaotong University, Xi’an, Shaanxi 710049, China;2. School of Electrical Engineering and Automation, Qilu University of Technology, Jinan, Shandong 250353, China;1. School of Telecommunication and Information Engineering, Xi’an University of Posts and Telecommunications, Xi’an 710121, PR China;2. Institute of Artificial Intelligence and Robotics, Xi’an Jiaotong University, Xi’an 710049, PR China |
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| Abstract: | 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. |
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| Keywords: | Sparse representation Discrimination Weighted KNN Video semantic concept detection |
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