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

Content Provider	Springer Nature Link
Author	Li, Honglin Toyoura, Masahiro Shimizu, Kazumi Yang, Wei Mao, Xiaoyang
Copyright Year	2016
Abstract	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.
Starting Page	1351
Ending Page	1363
Page Count	13
File Format	PDF
ISSN	01782789
Journal	The Visual Computer
Volume Number	32
Issue Number	10
e-ISSN	14322315
Language	English
Publisher	Springer Berlin Heidelberg
Publisher Date	2016-05-05
Publisher Place	Berlin, Heidelberg
Access Restriction	One Nation One Subscription (ONOS)
Subject Keyword	Content-based clothing image retrieval Collar design Feature extraction Saliency map SIFT Relevance feedback Optimum-path forest Computer Graphics Computer Science Artificial Intelligence (incl. Robotics) Image Processing and Computer Vision
Content Type	Text
Resource Type	Article
Subject	Computer Graphics and Computer-Aided Design Computer Vision and Pattern Recognition Software

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