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A new clothing image retrieval algorithm based on sketch component segmentation in mobile visual sensors
Author(s) -
Haopeng Lei,
Yugen Yi,
Yuhua Li,
Guoliang Luo,
Mingwen Wang
Publication year - 2018
Publication title -
international journal of distributed sensor networks
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.324
H-Index - 53
eISSN - 1550-1477
pISSN - 1550-1329
DOI - 10.1177/1550147718815627
Subject(s) - computer science , sketch , image retrieval , component (thermodynamics) , segmentation , visual word , artificial intelligence , weighting , clothing , classifier (uml) , image segmentation , feature (linguistics) , information retrieval , computer vision , pattern recognition (psychology) , image (mathematics) , algorithm , radiology , history , linguistics , medicine , physics , philosophy , archaeology , thermodynamics
Nowadays, the state-of-the-art mobile visual sensors technology makes it easy to collect a great number of clothing images. Accordingly, there is an increasing demand for a new efficient method to retrieve clothing images by using mobile visual sensors. Different from traditional keyword-based and content-based image retrieval techniques, sketch-based image retrieval provides a more intuitive and natural way for users to clarify their search need. However, this is a challenging problem due to the large discrepancy between sketches and images. To tackle this problem, we present a new sketch-based clothing image retrieval algorithm based on sketch component segmentation. The proposed strategy is to first collect a large scale of clothing sketches and images and tag with semantic component labels for training dataset, and then, we employ conditional random field model to train a classifier which is used to segment query sketch into different components. After that, several feature descriptors are fused to de...

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