Method for detecting judgment type visual saliency based on gradient direction histogram

A technology of gradient direction and detection method, applied in the field of computer vision, can solve the problems of no biological model, incomplete outline of objects, low resolution, etc.
CN101976338AActive Publication Date: 2011-02-16SERVICE CENT OF COMMLIZATION OF RES FINDINGS HAIAN COUNTY
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Patent Information

Authority / Receiving Office
CN Β· China
Patent Type
Applications(China)
Current Assignee / Owner
SERVICE CENT OF COMMLIZATION OF RES FINDINGS HAIAN COUNTY
Publication Date
2011-02-16

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Abstract

The invention relates to a method for detecting judgment type visual saliency based on a gradient direction histogram. The convention method has low resolution ratio, incomplete extracted object outline and high computation complexity. The method comprises the following steps of: firstly, extracting a color component diagram of an original image in a CIELAB space by a color transformation method;secondly, counting a local gradient direction histogram of every pixel point in the component diagram by using a sampling point in a central-peripheral computation structure; thirdly, calculating a local saliency judgment vector and a global saliency judgment vector of every pixel point; and finally, obtaining a final saliency judgment result by a linear weighting method. The method of the invention has a stronger response in a visually salient area and has higher inhibiting capacity in other non-salient areas.
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Description

technical field

[0001] The invention belongs to the field of computer vision, and in particular relates to a judgment-type central-peripheral visual saliency detection method based on a gradient direction histogram. Background technique

[0002] Visual saliency plays an important role in the visual selective attention mechanism. Inspired by the biological visual system, the visual saliency computational model provides a fast and effective method for reducing the computational complexity of computer vision processing, and has been widely used in machine Vision, image processing, intelligent robots and other fields.

[0003] Currently, most visual saliency models are based on spatial (bottom-up) or object-based (top-down) processes. The former is based on feature integration theory and central-peripheral hypothesis, and its representative work is the computational model proposed by L.Itti et al. In their model, the input image is decomposed into multiple independent feature ...

Claims

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