Feature Fusion based Hashing for Large Scale Image Copy Detection

Abstract

Currently, researches on content based image copy detection mainly focus on robust feature extraction. However, most of existing approaches use only a single feature to represent an image for copy detection, which is often insufficient to characterize the image content. Besides, with the exponential growth of online images, it's urgent to explore a way of tackling the problem of large scale. In this paper, we propose a feature fusion based hashing method which effectively utilize the correlation between two feature models and efficiently accomplish large scale image copy detection. To accurately map images into the Hamming space, our hashing method not only preserves the local structure of individual feature but also globally consider the local structures for all the features to learn a group of hash functions. The experiment results show that the proposed method outperforms the state-of-the-art techniques in both accuracy and efficiency.

Keywords

• Content Based Copy Detection
• Feature Fusion
• Kernel Canonical Correlation Analysis
• Neighborhood Structure Preserving Hashing