Publication Type
Journal Article
Version
publishedVersion
Publication Date
4-2014
Abstract
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.
Keywords
Algorithms, Experimentation, Performance, Bag-of-visual word, Hamming embedding, kernel optimization, video semantic indexing
Discipline
Artificial Intelligence and Robotics | Theory and Algorithms
Research Areas
Intelligent Systems and Optimization
Publication
ACM Transactions on Multimedia Computing, Communications and Applications
Volume
10
Issue
3
First Page
1
Last Page
20
ISSN
1551-6857
Identifier
10.1145/2535938
Publisher
Association for Computing Machinery (ACM)
Citation
1
Creative Commons License
This work is licensed under a Creative Commons Attribution-NonCommercial-No Derivative Works 4.0 International License.