Clothes search in consumer photos via color matching and attribute learning

Wang, Xianwang; Zhang, Tong

doi:10.1145/2072298.2072013

Cited by 93 publications

(44 citation statements)

References 9 publications

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“…Liu utilized middle-level clothing attributes (e.g., clothing category, pattern) as latent variables in latent Support Vector Machine (SVM) for recommendations. Wang et al [19] proposed a re-ranking system for apparel recommendations. First, the system extracted color features and retrieved by bag-of-visual features (BOW).…”

Section: Introductionmentioning

confidence: 99%

Content Based Image Search for Clothing Recommendations in E-Commerce

Wang

Zhou

Xiao

et al. 2015

Multimedia Data Mining and Analytics

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A number of algorithms exist in measuring clothing similarity for clothing recommendations in E-commerce. The clothing similarity mostly depends on its shape, texture and style. In this paper we introduce three models of defining feature space for clothing recommendations. The sketch-based image search mainly focuses on defining similarity of clothing in contour dimension. The spatial bagof-feature approach is employed to measure the clothing similarity of local image patterns. Finally, we introduce a query adaptive shape model which combines shape characteristics and labels of clothing, in order to take the semantic information of clothing. A large number of simulations are given to show the feasibility and performance of the clothing recommendations by using content-based image search.

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Section: Introductionmentioning

confidence: 99%

Content Based Image Search for Clothing Recommendations in E-Commerce

Wang

Zhou

Xiao

et al. 2015

Multimedia Data Mining and Analytics

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“…Huang et al used color features, texture features and skin features to represent clothing for a portraits ranking system [11]. Meanwhile, Wang et al used the Bow framework to search clothes by combining color features and attribute features [13]. Liu et al proposed ''color'', ''pattern'', ''material'' and ''shape'' features for cross-scenario clothing retrieval [12].…”

Section: Related Workmentioning

confidence: 99%

Finding suits in images of people in unconstrained environments

Yan

Huang

Wei

et al. 2014

Journal of Visual Communication and Image Representation

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“…In contrast, methods like [15] and ours focus on centered, cropped and pre-processed images with uniform background, which are the rule in a businessto-business setting. Our contribution.…”

Section: Introductionmentioning

confidence: 99%

“…Due to the importance of the market for online shopping of dressed clothing, various methods have been recently proposed to tackle the problem of classification of apparel images [11,12,13,14,15]. Most of them work with images taken in unconstrained environments and try to classify the apparel [11,13], retrieve related images in the database [12] or infer how much a person displays a certain style [14].…”

Section: Introductionmentioning

confidence: 99%

Discriminative learning of apparel features

Rothe

Ristin

Dantone

et al. 2015

2015 14th IAPR International Conference on Machine Vision Applications (MVA)

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Fashion is a major segment in e-commerce with growing importance and a steadily increasing number of products. Since manual annotation of apparel items is very tedious, the product databases need to be organized automatically, e.g. by image classification. Common image classification approaches are based on features engineered for general purposes which perform poorly on specific images of apparel. We therefore propose to learn discriminative features based on a small set of annotated images. We experimentally evaluate our method on a dataset with 30,000 images containing apparel items, and compare it to other engineered and learned sets of features. The classification accuracy of our features is significantly superior to designed HOG and SIFT features (43.7% and 16.1% relative improvement, respectively). Our method allows for fast feature extraction and training, is easy to implement and, unlike deep convolutional networks, does not require powerful dedicated hardware.

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Clothes search in consumer photos via color matching and attribute learning

Cited by 93 publications

References 9 publications

Content Based Image Search for Clothing Recommendations in E-Commerce

Content Based Image Search for Clothing Recommendations in E-Commerce

Finding suits in images of people in unconstrained environments

Discriminative learning of apparel features

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