Partial-Duplicate Clustering and Visual Pattern Discovery on Web Scale Image Database

Abstract

In this paper, we study the problem of discovering visual patterns and partial-duplicate images, which is fundamental to visual concept representation and image parsing, but very challenging when the database is extremely large, such as billions of images indexed by a commercial search engine. Although extensive research with sophisticated algorithms has been conducted for either partial-duplicate clustering or visual pattern discovery, most of them can not be easily extended to this scale, since both are clustering problems in nature and require pairwise comparisons. To tackle this computational challenge, we introduce a novel and highly parallelizable framework to discover partial-duplicate images and visual patterns in a unified way in distributed computing systems. We emphasize the nested property of local features, and propose the generalized nested feature (GNF) as a mid-level representation for regions and local patterns. Initial coarse clusters are then discovered by GNFs, upon which $n$ -gram GNF is defined to represent co-occurrent visual patterns. After that, efficient merging and refining algorithms are used to get the partial-duplicate clusters, and logical combinations of probabilistic GNF models are leveraged to represent the visual patterns of partially duplicate images. Extensive experiments show the parallelizable property and effectiveness of the algorithms on both partial-duplicate clustering and visual pattern discovery. With 2000 machines, it costs about eight and 400 minutes to process one million and 40 million images respectively, which is quite efficient compared to previous methods.