A Discriminative Locality-Sensitive Dictionary Learning With Kernel Weighted KNN Classification for Video Semantic Concepts Analysis

Author:

Ghansah Benjamin1ORCID,Benuwa Ben-Bright1ORCID,Monney Augustine1

Affiliation:

1. University of Education, Winneba, Ghana

Abstract

Video semantic concept analysis has received a lot of research attention in the area of human computer interactions in recent times. Reconstruction error classification methods based on sparse coefficients do not consider discrimination, essential for classification performance between video samples. To further improve the accuracy of video semantic classification, a video semantic concept classification approach based on sparse coefficient vector (SCV) and a kernel-based weighted KNN (KWKNN) is proposed in this paper. In the proposed approach, a loss function that integrates reconstruction error and discrimination is put forward. The authors calculate the loss function value between the test sample and training samples from each class according to the loss function criterion, and then vote on statistical results. Finally, this paper modifies the vote results combined with the kernel weight coefficient of each class and determine the video semantic concept. The experimental results show that this method effectively improves the classification accuracy for video semantic analysis and shorten the time used in the semantic classification compared with some baseline approaches.

Publisher

IGI Global

Subject

Decision Sciences (miscellaneous),Information Systems

Cited by 3 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献

1. Security Protection Technology Based on Intelligent Semantic Analysis;The 2021 International Conference on Smart Technologies and Systems for Internet of Things;2022-07-03

2. Virtual Kernel Discriminative Dictionary Learning With Weighted KNN for Video Analysis;International Journal of Data Analytics;2022-03-18

3. Credit risk assessment of small and medium-sized enterprises in supply chain finance based on SVM and BP neural network;Neural Computing and Applications;2022-01-15

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