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Li, H.; Wu, H.; Li, D.; Lin, S.; Su, Z.; Luo, X.: PSI: A probabilistic semantic interpretable framework for fine-grained image ranking (2018)
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- Abstract
- Image Ranking is one of the key problems in information science research area. However, most current methods focus on increasing the performance, leaving the semantic gap problem, which refers to the learned ranking models are hard to be understood, remaining intact. Therefore, in this article, we aim at learning an interpretable ranking model to tackle the semantic gap in fine-grained image ranking. We propose to combine attribute-based representation and online passive-aggressive (PA) learning based ranking models to achieve this goal. Besides, considering the highly localized instances in fine-grained image ranking, we introduce a supervised constrained clustering method to gather class-balanced training instances for local PA-based models, and incorporate the learned local models into a unified probabilistic framework. Extensive experiments on the benchmark demonstrate that the proposed framework outperforms state-of-the-art methods in terms of accuracy and speed.
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Lin, S.; Xie, I.: Behavioral changes in transmuting multisession successive searches over the web (2013)
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- Abstract
- Multisession successive information searches are common but little research has focused on quantitative analysis. This article enhances our understanding of successive information searches by employing an experimental method to observe whether and how the behavioral characteristics of searchers statistically significantly changed over sessions. It focuses on a specific type of successive search called transmuting successive searches, in which searchers learn about and gradually refine their information problems during the course of the information search. The results show that searchers' behavioral characteristics indeed exhibit different patterns in different sessions. The identification of the behavioral characteristics can help information retrieval systems to detect stages or sessions of the information search process. The findings also help validate a theoretical framework to explain successive searches and suggest system requirements for supporting the associated search behavior. The study is one of the first to not only test for statistical significance among research propositions concerning successive searches but to also apply the research principles of implicit relevance feedback to successive searches.
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Li, Y.; Xu, S.; Luo, X.; Lin, S.: ¬A new algorithm for product image search based on salient edge characterization (2014)
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- Abstract
- 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.
