A hamming embedding kernel with informative bag-of-visual words for video semantic indexing

• Main Authors: WANG, Feng, ZHAO, Wen-Lei, NGO, Chong-wah, MERIALDO, Bernard
• Format: text
• Language: English
• Published: Institutional Knowledge at Singapore Management University 2014
• Subjects: Algorithms; Experimentation; Performance; Bag-of-visual word; Hamming embedding; kernel optimization; video semantic indexing; Artificial Intelligence and Robotics; Theory and Algorithms
• Online Access: https://ink.library.smu.edu.sg/sis_research/6312; https://ink.library.smu.edu.sg/context/sis_research/article/7315/viewcontent/tomccap14_fwang.pdf
• Institution: Singapore Management University

In this article, we propose a novel Hamming embedding kernel with informative bag-of-visual words to address two main problems existing in traditional BoW approaches for video semantic indexing. First, Hamming embedding is employed to alleviate the information loss caused by SIFT quantization. The Hamming distances between keypoints in the same cell are calculated and integrated into the SVM kernel to better discriminate different image samples. Second, to highlight the concept-specific visual information, we propose to weight the visual words according to their informativeness for detecting specific concepts. We show that our proposed kernels can significantly improve the performance of concept detection.