Image summarization method based on hidden structure learning

Abstract

The invention discloses an image summary generation method based on hidden structure learning. It includes the following steps: 1) extract HSV color histogram features, visual word features and direction histogram features from the picture; 2) perform normalized preprocessing on the three features extracted in the previous step and normalize the three features 3) Construct a structural support vector machine with hidden variables, select the training set from the database multiple times, and use the picture sets related to different topics in the training set to learn the weight coefficient; 4) Use the above The weight coefficient obtained by one-step learning, selects different topic-related image sets from the database, predicts their implicit feature selection preferences, and generates a corresponding abstract image set. The present invention has higher information coverage and lower redundancy, and can implicitly learn different preferences in feature selection of image collections related to different topics, and achieves better results than traditional methods.

Description

technical field [0001] The invention relates to image summary generation, in particular to an image summary generation method based on hidden structure learning. Background technique [0002] At present, with the development of storage technology and network technology, a large number of image files are uploaded on the Internet every day. On the one hand, users can access more and more image data, on the other hand, the increasingly complex structure and redundancy of the data itself also bring great difficulties to users to obtain useful information. For example, users can use search engines to search a large number of pictures through keywords, but these pictures contain a large number of heavily similar pictures, which hinders the overall presentation of information, and a summary that takes into account information coverage and redundancy becomes more reasonable. Another example is that there are a large number of personal photo albums on the photo sharing website Flick...

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Problems solved by technology

Third, the differences in topics, that is, the information contained and preferred by different topic-related image sets are different

Because many traditional summarization methods often consider each picture in the summation independently, and do not comprehensively consider the complementary information and redundant information contained in each picture in the summarization picture set, so they cannot comprehensively analyze the original picture set and get a good result. summary of

In addition, although some summary generation methods proposed in the past consider the collection of summary pictures as a whole structure, they do not take into account the differences of related topics, so they cannot well adapt to user needs.

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