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)

Share

COinS