Feature fusing system and method for low-level visual features and text description information of images in social media

Abstract

The invention discloses a feature fusing system and method for low-level visual features and text description information of images in social media. The method includes the steps that word segmentation and other processing are performed on the text description information of the images, so that a text description set, with a word as the unit, of each image is generated, and words which occur in the text description sets in a whole image set and the global occurrence probabilities of the words are counted; the visual features of the images are extracted, wherein the visual features comprise normalized HSV spatial color histogram and edge direction histogram features; according to the low-level features of the images, the visual similarity degrees among the images are calculated, and for each image, k images which are highest in visual similarity degree are taken to generate a neighboring image set; feature fusing is performed on the visual features and the text description information of the images, and the relevancy between a word and a target image is calculated according to the local probabilities of occurring, in image neighbors, of the word in a text description set of the target image and the global probability of occurring, in all the images, of the word. The accuracy of the text description information of the images can be improved.

Description

Technical field [0001] The invention relates to the field of image retrieval in social media, in particular to a feature fusion system and method for image low-level visual features and image text description information, which calculates the correlation between text description information and images. Background technique [0002] With the rapid development of modern multimedia technology and network technology and the rise of social media, more and more users are keen to transmit and share images in social media. When uploading images, people may also provide the title and shooting time of the image. , Image content and other text description information. At present, more and more social media sharing platforms (such as Flickr, etc.) provide people with labeling services. People can label images by tagging. The labeling service enriches the text description information of images to a large extent. With the rapid increase in the number of images in social media, how to quickly ...

AI Technical Summary

Problems solved by technology

[0006] Text-based image retrieval relies on text keywords, and image retrieval only indexes and matches these text descriptions; however, it is difficult for text labels to fully express rich image content, and the manual labeling workload is huge, and there are error-prone, general Disadvantages such as specialization and strong subjectivity

Content-based image retrieval is still a very challenging research. The core issue is how to describe the image content. This type of image retrieval mainly focuses on the extraction of low-level visual features such as color, texture, and outline lights. However, due to The complexity of the description and extraction of low-level image features and the similarity measurement between features cannot completely solve the "semantic gap" between high-level semantics and low-level features, and its technology is still immature

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