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Retrieval of clothing images based on relevance feedback with focus on collar designs

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The content-based image retrieval methods are developed to help people find what they desire based on preferred images instead of linguistic information. This paper focuses on capturing the image features representing details of the collar designs, which is important for people to choose clothing. The quality of the feature extraction methods is important for the queries. This paper presents several new methods for the collar-design feature extraction. A prototype of clothing image retrieval system based on relevance feedback approach and optimum-path forest algorithm is also developed to improve the query results and allows users to find clothing image of more preferred design. A series of experiments are conducted to test the qualities of the feature extraction methods and validate the effectiveness and efficiency of the RF-OPF prototype from multiple aspects. The evaluation scores of initial query results are used to test the qualities of the feature extraction methods. The average scores of all RF steps, the average numbers of RF iterations taken before achieving desired results and the score transition of RF iterations are used to validate the effectiveness and efficiency of the proposed RF-OPF prototype.

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We would like to thank Prof Takami Yamamoto for her valuable comments. This work was partially supported by JSPS KAKENHI (16H05867, 25280037).

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Correspondence to Xiaoyang Mao.

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Li, H., Toyoura, M., Shimizu, K. et al. Retrieval of clothing images based on relevance feedback with focus on collar designs. Vis Comput 32, 1351–1363 (2016).

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