Image set abstract generation algorithm for social media

Abstract

The invention discloses an image set abstract generation algorithm for social media. The image set abstract generation algorithm for the social media comprises the first step of extracting visual features of images in an image set of the social media, the second step of conducting fusing and matching on extracted local features and global features, the third step of forming a space position map and a simplified geometric constraint representative data set according to the spatial constraint relations between optimum feature points and then filtering out noise points, the fourth step of establishing an automatic clustering model based on affinity propagation, and the fifth step of searching for a set of optimum class centers through a cluster validity index. By means of the algorithm, the multi-mode features of the images can be fully utilized, a large number of related images in the image set of the social image are abstracted, and consequently a set of optimum representative images are accurately generated.

Description

technical field [0001] The invention relates to the field of analysis and retrieval of social media image sets, in particular to an algorithm for generating abstracts of image sets under social media. Background technique [0002] With the rapid development of Internet technology and the wide application of multimedia technology, the status of the Internet has been continuously improved, and it has become the main platform for people to communicate and share information. Network users spontaneously contribute multimedia materials such as pictures and videos, and disseminate them through sharing, evaluation, discussion, etc., so that a large number of pictures flood into the network. In such a vast ocean of pictures, it is not easy to find the one we want. When we initiate a query, what is returned to us is a huge queue of multimedia picture sets, and there is a large amount of data with repeated or partially repeated content, as well as a considerable amount of data that is...

AI Technical Summary

Problems solved by technology

The smaller the number of clusters, the simpler the calculation but the lower the accuracy of the data set division. The higher the number of clusters, the higher the accuracy of the data set division but the redundancy of the summary image.

[0006] Second, traditional clustering algorithms rely on initial cluster centers, so the results are often unstable

[0007] Third, an optimal clustering algorithm does not exist, and no clustering method is suitable for all data sets

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