Image feature representation method based on multi-semantic codebook

Abstract

The present invention relates to an image feature representation method based on a multi-semantic codebook. The method performs the following processing on the images in the input training set: Step 1: intensively calculate image local features on the input image, and combine all local features According to the given semantic annotations, it is divided into several categories; the second step: according to the local features of multiple semantic categories in the first step, an optimization problem of joint learning is established, and a global codebook and multiple semantic codebooks are obtained by solving; the third step: Use the local features of each semantic category to train the corresponding semantic classifier for each semantic category; the fourth step: use the global codebook, semantic codebook, and semantic classifier to perform context-based feature quantization and semantic aggregation on the image, and finally represent into an image feature vector, that is, an image representation. Experiments prove that this method can represent the visual features of images more finely, and has higher accuracy in scene recognition than traditional methods.

Description

technical field [0001] The invention relates to a method in the technical field of computer vision for signal processing, in particular to an image feature representation method based on a multi-semantic codebook. Background technique [0002] The basic framework of the traditional image classification algorithm based on the Bag-of-Words Model mainly includes four parts: (1) feature extraction; (2) feature quantification; (3) feature aggregation; (4) image classification . The first step of feature extraction intensively calculates a large number of local features at various positions and scales of the image. Commonly used local image features include SIFT, HOG, LBP, etc. The second step of feature quantization is to quantize each feature into a discrete value according to a given codebook, which is generally the sequence number of the codeword closest to the feature vector in the codebook. The codebook can be obtained through sample clustering, and commonly used methods ...

AI Technical Summary

Problems solved by technology

However, the corresponding relationship obtained by artificially dividing blocks is too rough and does not conform to the real spatial distribution relationship of each element in the image.

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