Self-adaptive training sample selection method for relevance feedback image retrieval

Abstract

The invention discloses a self-adaptive training sample selection method for relevance feedback image retrieval, and the self-adaptive training sample selection method can increase the sample quantity and the positive sample proportion. The method includes the steps that bottom layer visual features of each image in an image library are extracted, and then the extracted features are stored in a feature library; similarity comparison is carried out on the features of each image in the feature library and the features of example images selected by users, and finally N images which are most similar to the example images are returned to the users; the users mark the most similar N images as direct proportion images or inverse proportion images; the marked images are stored in a support vector machine for training and learning, finally, the results are fed back to the users after learning, if the users are satisfied with the feedback results, the results can be outputted, and if not, the step continues to be carried out.

Description

technical field [0001] The invention belongs to the field of correlation feedback image retrieval of multimedia information processing, in particular to an adaptive training sample selection method for correlation feedback image retrieval which can increase the number of samples and the proportion of positive samples. Background technique [0002] At present, with the rapid development of multimedia technology and the increasing popularity of Internet technology, the sources of digital images are becoming more and more extensive, and image information of several gigabytes will be generated in various fields every day. In order to quickly and accurately find what users need from a large amount of image data, content-based image retrieval technology has attracted widespread attention and has become a research hotspot in the field of information retrieval, and has been extensively studied by international academic circles. The so-called content-based image retrieval is based on...

AI Technical Summary

Problems solved by technology

Among them, the number of samples increased through feedback results is very small, which cannot meet the needs of feedback; the number of samples is greatly increased through random selection methods, but this method can only increase counterexample samples, and it is not sure whether it can really be achieved in the selected samples. Represents negative examples; clustering algorithm for the image library, like the first method, the number of each increase is small, and the feedback effect cannot be achieved ideally

That is to say, the existing methods have the problem that the lack of training samples causes instability and the positive samples are far less than the negative samples, which causes the optimal hyperplane shift of the classifier.

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