Garment image retrieval method fusing color feature and residual network depth feature

Abstract

The invention belongs to the technical field of image retrieval, and discloses a garment image retrieval method fusing color features and residual network depth features, which comprises the followingsteps: inputting a training data set into a ResNet50-based network model; fusing the deep features and the color feature information to serve as global feature representation of the image; clusteringthe vectors in the feature library by using a K-Means algorithm; inputting a to-be-retrieved garment picture into the neural network the same as the data set, and obtaining a global feature vector ofthe to-be-queried garment picture; and sequentially calculating the distances between the vectors of the clustering centers and the vectors of the to-be-retrieved pictures, and performing similaritymeasurement through comparison of the distances to obtain a retrieval result. Experimental results show that the method can be combined with various feature information of the picture, the retrieval efficiency is high, and the time overhead is small. The extracted deep features have certain effectiveness and hierarchy. The method has high robustness and practicability and is superior to other mainstream retrieval methods.

Description

technical field [0001] The invention belongs to the technical field of image retrieval, and in particular relates to a clothing image retrieval method which combines color features and residual network depth features. Background technique [0002] Currently, the closest prior art: [0003] With the rapid development of the e-commerce industry, the clothing industry, as an important part of it, has an increasing amount of data. In order to deal with massive clothing image data, a new online clothing search mode is used by users - "searching images with pictures ", the core of which is image retrieval technology. As the core of clothing intelligent recommendation, clothing search and other applications, clothing image retrieval has broad market application prospects. Clothing shows a trend and taste of contemporary people, and a lot of semantic and detailed information is contained in it. The color matching and style of clothing are its important semantic information, and th...

AI Technical Summary

Problems solved by technology

[0007] (1) The existing technology only extracts the fully connected layer and cannot maintain the spatial structure, and this feature is more representative of global information, and the local feature information of the clothing picture is lost, resulting in a low average precision of retrieval

[0008] (2) When the residual network is directly applied to clothing image retrieval, the retrieved pictures often have the problem of similar styles but large color differences

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