Abstract
In visual search systems, it is important to address the issue of how to leverage the rich contextual information in a visual computational model to build more robust visual search systems and to better satisfy the user’s need and intention. In this paper, we introduced a ranking model by understanding the complex relations within product visual and textual information in visual search systems. To understand their complex relations, we focused on using graph-based paradigms to model the relations among product images, product category labels, and product names and descriptions. We developed a unified probabilistic hypergraph ranking algorithm, which, modeling the correlations among product visual features and textual features, extensively enriches the description of the image. We conducted experiments on the proposed ranking algorithm on a dataset collected from a real e-commerce website. The results of our comparison demonstrate that our proposed algorithm extensively improves the retrieval performance over the visual distance based ranking.
Highlights
Ranking plays an essential role in a product search system
We build the unified images hypergraph using different combinations of hyperedges to test the effect of different factors on the ranking performance
We investigate the performance of different hypergraphs
Summary
Ranking plays an essential role in a product search system. Given a query, candidate products should be ranked according to their distance to the query. Their research [3, 4] proves the effectiveness of hypergraph learning in solving ranking problems They fail to establish correlations between images visual content and textual content. In order to solve this issue and minimize user’s efforts in query, we take the product rich textual information into the visual model of image retrieval and propose a novel hypergraphbased transductive algorithm for ranking of visual product search. We design a unified probabilistic hypergraph to model multiple types of features of the products and explore the implicit relations among various visual and textual features. Since relations between visual features and textual features have been established in a specific unified probabilistic hypergraph, problems that lack user-labeled query keywords can be solved via transductive inference on the hypergraph.
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