Supervised image classification method based on self-paced constraint mechanism

Abstract

The invention discloses a supervised image classification method based on a self-paced constraint mechanism. The method comprises the following steps: dividing difficult types of training samples; establishing a sparse representation model, and substituting samples into the sparse representation model for training; obtaining an image classification model and a prediction model; constructing a category decision-making device, wherein the training sample difficult type comprises a training easy sample and a training difficult sample, and the division training sample difficultly-easy types are divided by adopting a self-step constraint matrix. According to the invention, the training samples are divided through the self-paced constraint matrix; the easy training sample and the difficult training sample are sequentially substituted into a defined sparse representation model for continuous training; a specific self-step constrained image classification scheme can be formed, more judgment information can be conveniently utilized, robustness is achieved on sample noise, and therefore the problem that a supervised dictionary learning mechanism is not suitable for complex samples containingnoise and huge intra-class changes can be solved, and the image recognition effect is improved.

Description

technical field [0001] The invention relates to the technical field of image classification and recognition, in particular to a supervised image classification method based on a self-procedural constraint mechanism. Background technique [0002] Classifying natural images containing a large number of object categories is one of the most challenging problems in pattern recognition, and mainstream solutions include wavelet relational vector machines (WRVM), global and local saliency feature encoding, and bag-of-words models (bow), Previous image classification algorithms have focused on obtaining visual representations of image features while ignoring specific class information. In order to find a more suitable method for data representation, a large number of schemes have been developed to solve this problem. Recently, these have been developed In the model of , the sparse representation classification technology with supervision has attracted many people's interest because o...

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Problems solved by technology

[0002] Classifying natural images containing a large number of object categories is one of the most challenging problems in pattern recognition, and mainstream solutions include wavelet relational vector machines (WRVM), global and local saliency feature encoding, and bag-of-words models (bow), Previous image classification algorithms have focused on obtaining visual representations of image features while ignoring specific class information. In order to find a more suitable method for data representation, a large number of schemes have been developed to solve this problem. Recently, these have been developed In the model of , the sparse representation classification technology with supervision has attracted many people's interest because of its powerful image modeling ability. Many studies have shown that this kind of sparse representation-based classification (SRC) algorithm has a very Excellent, however, the supervised dictionary learning mechanism is no longer applicable when faced with complex samples containing noise and large intra-class variation, moreover, learning a discriminative and representative dictionary from complex training samples is still a challenge

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