Significance detection method based on selection of regional candidate sample

A technology of candidate samples and detection methods, which is applied in image data processing, instruments, character and pattern recognition, etc., can solve problems such as inaccurate detection results, and achieve the effect of accurate saliency map and accurate detection results

Active Publication Date: 2017-08-29
DALIAN UNIV OF TECH
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Problems solved by technology

There are many deficiencies in the traditional saliency detection methods, especially when faced with complex multi-target images or situations where the salient targets are very similar to the background, the detection results are often inaccurate

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  • Significance detection method based on selection of regional candidate sample
  • Significance detection method based on selection of regional candidate sample
  • Significance detection method based on selection of regional candidate sample

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Embodiment Construction

[0024] The specific embodiments of the present invention will be further described below with reference to the accompanying drawings and technical solutions.

[0025] The idea of ​​the present invention is to select the optimal region candidate sample and use it for salient target detection by defining the evaluation index for evaluating the targetness and saliency of the region candidate sample in combination with the existing prior knowledge. In the detection process, in addition to the traditional prior knowledge such as the contrast around the center, the internal similarity, and the position prior, the contour information of the region candidate samples is also evaluated from the global and local perspectives. In order to describe the region candidate samples more accurately, we also introduce depth features to make the detection results more in line with human visual perception. Further, the present invention also introduces a structured classifier to optimize the select...

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Abstract

The invention provides a significance detection method based on selection of a regional candidate sample, and belongs to the technical field of artificial intelligence. On the basis of existing prior knowledge, depth characteristics and a classifier are introduced, and a selecting mechanism from rough to fine is used, the significance and targeted performance of the regional candidate sample are evaluated, a detection result is further optimized by utilizing super pixels, and a significant object in an image can be detected effectively. Compared with a traditional method, the detection result is more accurate. Especially for a multi-object image or an image in which the object is very similar to background, the detection result satisfies visual perception of humans more, and an obtained significance map is more accurate.

Description

technical field [0001] The invention belongs to the technical field of artificial intelligence, relates to computer vision, and particularly relates to an image saliency detection method. Background technique [0002] With the development of science and technology, the images, videos and other information received by people show an explosive growth. How to process image data quickly and effectively has become an urgent problem to be solved. Usually, people only focus on the more salient areas in the image that attract the attention of the human eye, that is, foreground areas or salient objects, while ignoring background areas. Therefore, people use computers to simulate the human visual system for saliency detection. At present, saliency research can be widely applied to various fields of computer vision, including image retrieval, image compression, object recognition, and image segmentation. [0003] In saliency detection, how to accurately detect salient objects from i...

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06T7/11G06T7/194G06K9/32G06K9/34
CPCG06T7/11G06T7/194G06T2207/30196G06T2207/10004G06T2207/20081G06V10/25G06V10/267
Inventor 张立和周钦
Owner DALIAN UNIV OF TECH
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