Deep locality‐sensitive discriminative dictionary learning for semantic video analysis. (29th January 2020)

• Record Type: Journal Article
• Authors: Benuwa, Ben‐Bright; Ghansah, Benjamin; Ansah, Ernest K.
• Is Part Of: Software, practice & experience. Volume 50:Number 4(2020)
• Page Start: 388
• Page End: 406
• Publication Date: 2020-01-29
• Subjects: deep learning -- dictionary learning -- kernel density estimator -- locality information -- sparse representation -- video semantic analysis; Computer software -- Periodicals; Computer programming -- Periodicals; Computer programs -- Periodicals; 005.3
• DOI: 10.1002/spe.2783
• Languages: English
• ISSNs: 0038-0644

Abstract

Summary: Video semantic analysis (VSA) has received significant attention in the area of Machine Learning for some time now, particularly video surveillance applications with sparse representation and dictionary learning. Studies have shown that the duo has significantly impacted on the classification performance of video detection analysis. In VSA, the locality structure of video semantic data containing more discriminative information is very essential for classification. However, there has been modest feat by the current SR‐based approaches to fully utilize the discriminative information for high performance. Furthermore, similar coding outcomes are missing from current video features with the same video category. To handle these issues, we first propose an improved deep learning algorithm—locality deep convolutional neural network algorithm (LDCNN) to better extract salient features and obtain local information from semantic video. Second, we propose a novel DL method, called deep locality‐sensitive discriminative dictionary learning (DLSDDL) for VSA. In the proposed DLSDDL, a discriminant loss function for the video category based on sparse coding of sparse coefficients is introduced into the structure of the locality‐sensitive dictionary learning (LSDL) 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 reducing the error existing between the original and recreated samples. The experiment results show that the proposed DLSDDL technique considerably increases the efficiency of video semantic detection as against competing methods used in our experiment.