Image classification method based on supplemented text characteristic

Abstract

The invention relates to the field of digital image processing technology, and provides a method for image classification through supplementing a text characteristic for settling a problem of classification result deterioration caused by image characteristic discriminating property reduction because of text characteristic information loss in a bag of visual words. The image classification method based on the supplemented text characteristic comprises the following steps of (1), respectively extracting an SIFT characteristic, a color characteristic, and a shape-and-boundary characteristic of an image in a data set; (2), randomly selecting 200000 characteristics from the supplemented text characteristics and obtaining a dictionary B through a K-singular value decomposition method (K-SVD); (3), obtaining a sparse characteristic vector C through the studied dictionary B; (4), performing spatial maximal value gathering on the sparse characteristic; and (5), transmitting an image characteristic vector F into a linear SVM classifier, thereby performing training and testing respectively for obtaining an image classification result. The image classification method is mainly applied for digital image processing.

Description

technical field [0001] The invention relates to the technical field of digital image processing, in particular to a scene image classification method based on supplementary text features. Background technique [0002] Image classification, as the basis of image understanding, plays an important role in the field of computer vision. Although people have made a lot of efforts and made great progress in feature extraction and training, there is still no unified best classification method suitable for all images. [0003] In image classification, one of the most popular is the visual bag-of-word model (Bag-of-Word), which is an image classification model based on a statistical model, and its purpose is to obtain a more expressive representation of image features. The most widely used image features in this model are features based on image text information, such as SIFT, HOG, etc. After extracting the features of the image, it is necessary to use the obtained local features to...

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Problems solved by technology

[0005] In order to overcome the deficiencies in the prior art, the present invention aims to: solve the problem that the image feature discrimination caused by the lack of text feature information in the visual bag-of-words model eventually affects the classification results, and propose a method for supplementing text features with for image classification

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