Image retrieval method combining visual saliency and phrases

Abstract

The invention discloses an image retrieval method combining visual saliency and phrases. The image retrieval method comprises the following steps that a search image is input and decoded into a YUV color space, and K-means clustering is conducted on pixels of the YUV color space, so that the image is divided into multiple superpixel units; likelihood computation is conducted on each superpixel unit of the divided image, and four obtained likelihoods are fused, and then the saliency map of the superpixel precision is obtained; bilateral Gaussian filtering is conducted, an image saliency map of the pixel precision is obtained; self-adaptive threshold segmentation is conducted on the image saliency map, and a binary image with a prominent target portion is obtained; a dictionary is established, vision words of an image salient region are extracted, and image description is generated; the image similarity between the search image and each image in a image base is calculated. By the adoption of the image retrieval method, the salient region in the image can be more accurately reflected, the visual saliency and the phases are well combined, and the retrieval effect is good.

Description

technical field [0001] The invention relates to the field of image processing, in particular to an image retrieval method combining visual salience and phrases. Background technique [0002] With the rapid development and application of computer, network and multimedia technology, the number of digital images is increasing at an alarming rate. How to quickly and efficiently find the images people need from the massive digital image collection has become an urgent problem to be solved. For this reason, image retrieval technology has emerged as the times require and has achieved great development. From the earliest retrieval based on manual annotation of images to the current retrieval based on image content, the accuracy and efficiency of image retrieval have also been significantly improved, but it is still not possible. satisfy people's demands. The crux of the problem is that there is currently no way to make a computer understand image semantics exactly like a human. If...

AI Technical Summary

Problems solved by technology

For this reason, image retrieval technology has emerged as the times require and has achieved great development. From the earliest retrieval based on manual annotation of images to the current retrieval based on image content, the accuracy and efficiency of image retrieval have also been significantly improved, but it is still not possible. satisfy people's demands

The crux of the problem is that there is no way to make a computer understand image semantics exactly like a human.

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