Adaptive Feature Extraction Method Based on Image Blocking

Abstract

The invention provides a self-adaptation feature extracting method based on image partitioning. The problem that according to an existing feature extracting method based on PCA, images need vectorization, so that recognition results are not ideal after feature extracting is mainly solved. The method comprises the steps that (1) an image set is input and is divided into a training image set and a testing image set randomly; (2) images in the training image set are partitioned, and training sub-block image sets are formed; (3) the pixel gray value variance sums of the training image set and the training sub-block image sets are computed respectively; (4) the pixel gray value variance sums of the training image set and the training sub-block image sets are compared, and a feature best projection matrix is gained; (5) the image features of the training image set and the testing image set are extracted; and (6) images of the testing image set are recognized, and feature extracting effect is verified. Compared with the prior art, the self-adaptation feature extracting method has the advantages of being high in recognition rate, wide in adaptability and the like, feature extracting is effectively carried out on the images, and the method can be used for targeting identification.

Description

technical field [0001] The invention belongs to the field of computer technology, and further relates to the field of computer image information processing, in particular to an adaptive feature extraction method based on image segmentation. The present invention obtains sub-block images by dividing the image into blocks, calculates the pixel gray value variance sum of the image and the pixel gray value variance sum of each sub-block image, and judges the size, and adaptively uses the two-dimensional Principal component analysis (Two Dimension Principal Component Analysis, 2DPCA) or wavelet principal component analysis (Wavelet Principal Component Analysis, Wavelet PCA) for feature extraction, effectively realizes the feature extraction of images, and provides reliable information for subsequent target recognition . Background technique [0002] As the basis of image target recognition, image feature extraction is a key technology in automatic target recognition. In recent ...

AI Technical Summary

Problems solved by technology

The disadvantage of the feature extraction method based on PCA is that, first of all, in the process of vectorizing the image, the dimension of the image vector will generally be very high, and the analysis on the high-dimensional image vector will encounter the problem of small samples, and It often takes a lot of time; secondly, PCA usually extracts the global features of the image, ignoring the structural information and local information of the image, and the recognition effect is not ideal

The disadvantage of this method is that although the wavelet transform is used to preprocess the image and the recognition rate has been improved, the wavelet transform only has a better effect on images with a relatively small degree of image change, and for images with a relatively large degree of change The effect of image preprocessing is not ideal, and this method still needs to vectorize the image, which destroys the structural information of the image, which is not conducive to achieving better recognition effect

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