Cloth image retrieval method based on convolutional neural network

Abstract

The invention discloses a cloth image retrieval method based on a convolutional neural network, and the method comprises the steps: carrying out preprocessing of a textile fabric image, zooming of theimage through bilinear interpolation, and carrying out the normalization and other preprocessing operations; designing a convolutional neural network as a classifier; training the neural network by using a classified loss function and gradient back propagation iteration to obtain a feature extractor; performing feature extraction on the retrieval graph and the fabric library to obtain a 1024-dimensional feature vector; and calculating the similarity of the two feature vectors by adopting an L2 measurement method, and sorting to realize recognition of textile fabric image retrieval. Accordingto the invention, contour spatial position feature extraction can be carried out on the target shape, and recognition of the target with occlusion is realized. The method has scale invariance, rotation invariance and translation invariance, so that the problem of incomplete contour recognition is effectively solved, and the accuracy and robustness of target recognition and shape retrieval are improved.

Description

technical field [0001] The invention relates to the field of image retrieval, in particular to a cloth image retrieval method based on a convolutional neural network. Background technique [0002] It has always been a challenge for fabric suppliers to quickly find the fabric that is most similar to the incoming sample cloth in a huge number of textile fabric varieties. [0003] One of the traditional search methods for fabrics and textiles is to rely mainly on human eyes. For more skilled master craftsmen, they may also rely on their own memory to speed up the search. However, this method becomes more difficult as the number of textile fabrics increases, and human memory is degraded and fuzzy, and it is prone to frequent mistakes, which affects retrieval efficiency. Another solution is to build an automated retrieval system based on traditional image features, such as image texture features, edge features, hash features, etc. These traditional features require manual exper...

AI Technical Summary

Problems solved by technology

These traditional features require manual experiments to set parameters, and the setting of parameters is not suitable for all types of textile fabric images. The main reason is that there are too many types of images, and the number of parameters is small, which cannot adapt to all types.

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