Publication Type

Journal Article

Version

publishedVersion

Publication Date

2-2014

Abstract

Near-duplicate retrieval (NDR) in merchandize images is of great importance to a lot of online applications on e-Commerce websites. In those applications where the requirement of response time is critical, however, the conventional techniques developed for a general purpose NDR are limited, because expensive post-processing like spatial verification or hashing is usually employed to compromise the quantization errors among the visual words used for the images. In this paper, we argue that most of the errors are introduced because of the quantization process where the visual words are considered individually, which has ignored the contextual relations among words. We propose a "spelling or phrase correction" like process for NDR, which extends the concept of collocations to visual domain for modeling the contextual relations. Binary quadratic programming is used to enforce the contextual consistency of words selected for an image, so that the errors (typos) are eliminated and the quality of the quantization process is improved. The experimental results show that the proposed method can improve the efficiency of NDR by reducing vocabulary size by 1000% times, and under the scenario of merchandize image NDR, the expensive local interest point feature used in conventional approaches can be replaced by color-moment feature, which reduces the time cost by 9202% while maintaining comparable performance to the state-of-the-art methods.

Keywords

Near-duplicate retrieval, visual word quantization, binary quadratic programming

Discipline

Graphics and Human Computer Interfaces | Programming Languages and Compilers

Research Areas

Intelligent Systems and Optimization

Publication

IEEE Transactions on Image Processing

Volume

23

Issue

2

First Page

527

Last Page

540

ISSN

1057-7149

Identifier

10.1109/TIP.2013.2293427

Publisher

Institute of Electrical and Electronics Engineers

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