Image feature extraction method based on weighted depth features

Abstract

The invention discloses an image feature extraction method based on weighted depth features, and the method comprises the following steps: selecting an existing network model for the pre-training of an image data set, and then removing a network prediction layer in the network model, and obtaining a final network model; inputting the to-be-extracted image into the final network model for forward calculation, and extracting convolution layers in front of all pooling layers in the final network model as a depth feature map of the image; calculating a feature aggregation vector for each convolution layer, and performing extraction and zero-mean normalization processing on each feature aggregation vector to obtain a depth feature vector; carrying out dimensionality reduction on all the depth feature vectors, and distributing the corresponding weight parameters to the depth feature vectors after dimensionality reduction to serve as indexes of each convolution layer; and fusing the depth feature vectors with the weight parameters to obtain depth image features. The method is high in universality, good in obtained feature robustness, high in expression capability, rich in semantic information and easy and convenient to calculate.

Description

technical field [0001] The present invention relates to the field of image processing, and more specifically, to an image feature extraction method based on weighted depth features. Background technique [0002] In recent years, with the explosion of image big data, technologies such as image recognition, retrieval, classification, positioning and detection have made great progress, and among these commonly used technologies, image feature extraction is the key. The quality of the final image task largely depends on the quality of image feature extraction. Therefore, how to extract a better image feature is the main research direction in this field. In traditional image classification, positioning and retrieval tasks, it is generally based on some basic features of the image, such as color, texture, etc. These features cannot solve the problems of scale change, occlusion, illumination, affine transformation, etc. The emergence of image feature extraction algorithms has sol...

AI Technical Summary

Problems solved by technology

[0005] The present invention provides an image feature extraction method based on weighted depth features in order to overcome the defects of poor versatility, complex calculation, and low robustness of the extracted features in the image big data feature extraction method in the prior art

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