A Hamming Embedding Kernel with Informative Bag-of-Visual Words for Video Semantic Indexing

Feng Wang, Wan-Lei Zhao, Chong-Wah Ngo, Bernard Merialdo
2014 ACM Transactions on Multimedia Computing, Communications, and Applications (TOMCCAP)  
In this paper, 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 SVM kernel to better discriminate different image samples. Second, to highlight the conceptspecific visual
more » ... ific 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.
doi:10.1145/2535938 fatcat:o5uwlminazdftfn2yzylthgggi