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A new algorithm for product image search based on salient edge characterization
Author(s) -
Li Yuhua,
Xu Songhua,
Luo Xiaonan,
Lin Shujin
Publication year - 2014
Publication title -
journal of the association for information science and technology
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.903
H-Index - 145
eISSN - 2330-1643
pISSN - 2330-1635
DOI - 10.1002/asi.23136
Subject(s) - computer science , similarity (geometry) , set (abstract data type) , salient , histogram , image retrieval , product (mathematics) , artificial intelligence , image (mathematics) , enhanced data rates for gsm evolution , computer vision , matching (statistics) , scale invariant feature transform , pairwise comparison , information retrieval , pattern recognition (psychology) , data mining , mathematics , geometry , programming language , statistics
Visually assisted product image search has gained increasing popularity because of its capability to greatly improve end users' e‐commerce shopping experiences. Different from general‐purpose content‐based image retrieval ( CBIR ) applications, the specific goal of product image search is to retrieve and rank relevant products from a large‐scale product database to visually assist a user's online shopping experience. In this paper, we explore the problem of product image search through salient edge characterization and analysis, for which we propose a novel image search method coupled with an interactive user region‐of‐interest indication function. Given a product image, the proposed approach first extracts an edge map, based on which contour curves are further extracted. We then segment the extracted contours into fragments according to the detected contour corners. After that, a set of salient edge elements is extracted from each product image. Based on salient edge elements matching and similarity evaluation, the method derives a new pairwise image similarity estimate. Using the new image similarity, we can then retrieve product images. To evaluate the performance of our algorithm, we conducted 120 sessions of querying experiments on a data set comprised of around 13k product images collected from multiple, real‐world e‐commerce websites. We compared the performance of the proposed method with that of a bag‐of‐words method (Philbin, Chum, Isard, Sivic, & Zisserman, 2008) and a Pyramid Histogram of Orientated Gradients ( PHOG ) method (Bosch, Zisserman, & Munoz, 2007). Experimental results demonstrate that the proposed method improves the performance of example‐based product image retrieval.