Fine-grained convolutional neural network-based clothes recommendation method

A technology of convolutional neural network and recommendation method, which is applied in the field of target detection and recommendation, can solve the problems of increasing the number of windows and poor real-time performance, and achieve the effects of reducing false detection rate, ensuring accuracy rate, and avoiding subjectivity

Inactive Publication Date: 2015-10-21
ZHEJIANG UNIV
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Problems solved by technology

On the other hand, traditional clothing detection methods usually use multi-scale sliding windows to traverse the image to find clothing targets. This method increases the number of windows that need to be judged, and the real-time performance is poor.

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Embodiment Construction

[0020] The technical solutions of the present invention will be clearly and completely explained below in conjunction with the accompanying drawings in the present invention.

[0021] The present invention proposes a clothing recommendation method based on a fine-grained convolutional neural network. The method first marks a large database containing clothing pictures and non-clothing pictures, and trains a fine-grained convolutional neural network on the labeled clothing pictures. The internet. In the process of testing, firstly, the local candidate areas of the test pictures are screened through the hierarchical target area selection algorithm, and the trained fine-grained convolutional neural network is used to identify the areas, and the nearest neighbor samples are found in the training pictures. Make recommendations. figure 1 It is a flow chart of the clothing recommendation method based on the fine-grained convolutional neural network of the present invention. Such as...

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Abstract

A fine-grained convolutional neural network-based clothes recommendation method. First clothes pictures of various types and pictures that do not contain clothes are collected from an e-commerce website, and pictures of the two categories are labeled. Then a convolutional neural network is initialized, the labeled pictures are used to train the convolutional neural network. A recommendation process first uses a segmentation-based target area selection method to select a candidate shape area that is possible to contain clothes in a picture input by a user, and searches for the closest picture in a training picture database through a specific distance function and indexes the corresponding clothes type labeling. Depth characteristics of all clothes areas are extracted, linking and recommendation grading are performed, and finally a recommendation result is returned to the user. The extracted depth characteristics have very good invariance for rotation, translation and scale changes, and in addition, the recommendation method based on the segmentation-based target area and the adjacent picture improves recommendation accuracy to some extent.

Description

technical field [0001] The invention belongs to the field of target detection and recommendation, and relates to a method for detecting and recommending clothes from Internet e-commerce pictures. Background technique [0002] At present, with the rapid development of Internet e-commerce, such as Taobao, JD.com, and Amazon, people often face the problem of too many goods and no choice when choosing clothes online. How to improve the existing e-commerce recommendation method and effectively filter the products through the user's purchasing habits, so as to ensure that the clothes that are more in line with the user's purchasing habits and dressing and matching habits can be recommended. It has become a problem that e-commerce giants spend a lot of money and manpower at this stage. [0003] Affected by scale transformation, clothing models, different scenes, occlusion, complex background interference, etc., it is a challenging task to accurately identify and recommend clothes ...

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06K9/62G06N3/02
CPCG06N3/02G06F18/214
Inventor 陈纯卜佳俊刘钊朱建科宋明黎
Owner ZHEJIANG UNIV
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