Revisiting Gist-PCA Hashing for Near Duplicate Image Detection

Abstract

This paper presents a scalable method of near duplicate image detection based on Gist-PCA (principal component analysis) hashing. While most of transform coding methods have been interested in nearest neighbor search with applications to similar image search, we solve a range search problems found in near duplicate detection problems. At first, we argue that the PCA hashing of the Gist descriptor is adequate for near duplicate image detection. Then, we decompose the Gist-PCA binary code into a hash key and a residual binary code for scalability into large-scale datasets. In addition, a multi-block approach is incorporated into the method to deal with strong variations, such as image cropping and border framing. Experimental results show that the proposed method is more accurate and faster than the real-valued Gist descriptor and other nearest neighbor search methods.