Image characteristic extracting and describing method

Abstract

The invention relates to the field of image processing and computer vision and particularly provides an image characteristic extracting and describing method which is suitable for a BoW (Bag of Words) model and is applied to the field of computer vision. The image characteristic extracting and describing method comprises the following steps of: carrying out format judgment on an input image, not processing if the input image is a gray level image and converting the input image into an HSV (Hue, Saturation, Value) model if the input image is not the gray level image; selecting scale parameters; by adopting a uniform sampling method, according to the selected scale parameters, extracting characteristic points of the image at equal pixel intervals, calculating DF-SIFT (Dense Fast-Scale Invariant Feature Transform) descriptors of an H (Hue) channel, an S (Saturation) channel and a V (Value) channel of the image, applying color information into a classification task and controlling the sampling density by a parameter step to obtain the dense characteristic of the image; and carrying out description on the dense characteristic. According to the invention, by densely sampling, a visual dictionary is more accurate and reliable; and the bilinear interpolation replaces the image and Gaussian kernel function convolution process, so that the implementing process is simpler and more efficient.

Description

technical field [0001] The invention relates to the fields of image processing and computer vision, and specifically provides an image feature extraction and description method applicable to the application of a BoW (Bag of Words) model in the field of computer vision. Background technique [0002] As a basic application of image processing, image classification has long been widely concerned by experts, scholars and engineers from various countries. The BoW model was originally applied in the field of document processing, representing the document as a combination of sequence-independent keywords, and matching by counting the frequency of keywords in the document. In recent years, researchers in the field of computer vision have successfully transplanted the idea of ​​this model to the field of image processing. By extracting and describing the features of the image, a large number of features are obtained for processing, so as to obtain the words used to represent the imag...

AI Technical Summary

Problems solved by technology

In the process of extracting and describing feature points, the complexity is high and a lot of computing time is consumed, which is also a disadvantage for image recognition and classification tasks.

In the BoW model, after the feature extraction process, the clustering method is applied to generate visual words. Therefore, if sufficient information cannot be provided in the feature extraction process, it will directly affect the representativeness of the generated visual words, thereby affecting Subsequent classification accuracy

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