A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2017; you can also visit the original URL.
The file type is
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 visualdoi:10.1145/2535938 fatcat:o5uwlminazdftfn2yzylthgggi