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Garment attribute retrieval method based on deep convolutional neural network

A convolutional neural network and neural network technology, applied in neural learning methods, biological neural network models, instruments, etc., can solve the problem of not improving the accuracy of clothing attribute prediction, and achieve the effect of improving accuracy

Active Publication Date: 2017-11-07
XIAN JIAOTONG LIVERPOOL UNIV
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Problems solved by technology

But it does not improve the accuracy of clothing attribute prediction

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  • Garment attribute retrieval method based on deep convolutional neural network
  • Garment attribute retrieval method based on deep convolutional neural network
  • Garment attribute retrieval method based on deep convolutional neural network

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[0033] In order to make the object, technical solution and advantages of the present invention clearer, the present invention will be further described in detail below in combination with specific embodiments and with reference to the accompanying drawings. It should be understood that these descriptions are exemplary only, and are not intended to limit the scope of the present invention. Also, in the following description, descriptions of well-known structures and techniques are omitted to avoid unnecessarily obscuring the concept of the present invention.

[0034] Such as figure 1 As shown, the present invention is based on the clothing attribute retrieval method of depth convolutional neural network, comprises the following steps:

[0035] Step 1: Use a fast Region-based Convolutional Network (Fast Region-based Convolutional Network, Fast RCNN) as a target detector to detect portraits from complex background images. The specific steps of target detection are as follows: ...

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Abstract

The present invention discloses a garment attribute retrieval method based on a deep convolutional neural network. The method comprises: employing a rapid convolutional neural network based on an area to perform portrait detection of an input image; employing a pre-training deep convolutional neural network to perform attribute feature extraction, and retaining the features of a final pooling layer; employing a sharing layer to connect with the features retained by the pooling layer, and fusing the feature information of all the attributes; establishing an attribute tree, performing classification of garment attributes, performing branching of the sharing layer according to the classification, each attribute branching being configured for prediction of one group of related attributes; and performing attribute branching output series overlaying, performing normalization, performing similarity measurement through the locality-sensitive hashing method, and obtaining a result. The feature description of the garment attributes is used for garment attribute detection so as to observably improve the accuracy of prediction of garment attributes.

Description

technical field [0001] The invention relates to a clothing retrieval method based on a convolutional neural network, in particular to a clothing attribute retrieval method based on a deep convolutional neural network. Background technique [0002] With the rapid development of the Internet and clothing e-commerce, the online shopping market is expanding year by year. How to use retrieval technology to help users quickly find their favorite clothing is a very important task. However, the identification of clothing detection is very difficult: first, the shape of the clothes is large, and the clothes themselves are very flexible objects, and different postures of people will lead to different shapes of clothes; second, under different lighting conditions and complex scenes , the difficulty of distinguishing different types of clothing will also increase; in addition, the design of clothes contains a large number of detailed attributes, such as collar shape, version, color, dec...

Claims

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Application Information

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IPC IPC(8): G06K9/62G06N3/08
CPCG06N3/084G06F18/24G06F18/253G06F18/214
Inventor 张百灵夏翌彰武芳宇吕文进
Owner XIAN JIAOTONG LIVERPOOL UNIV
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