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Robust sparse representation and Laplace regular term-based salient object detection method

A sparse representation and target detection technology, applied in the field of image processing, can solve the problems of not considering the spatial local consistency and spatial feature consistency of images, and poor consistency of salient target detection results, and achieve the effect of improving the suppression effect.

Inactive Publication Date: 2017-10-27
重庆江雪科技有限公司
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Problems solved by technology

However, such methods only independently calculate the saliency value of each image superpixel, without considering the spatial local consistency and spatial feature consistency of the image, so the consistency of the salient object detection results is poor

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  • Robust sparse representation and Laplace regular term-based salient object detection method
  • Robust sparse representation and Laplace regular term-based salient object detection method
  • Robust sparse representation and Laplace regular term-based salient object detection method

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Embodiment Construction

[0032] The embodiments and effects of the present invention will be described in further detail below with reference to the accompanying drawings.

[0033] Reference figure 1 , The implementation steps of the present invention are as follows:

[0034] Step 1. Perform over-segmentation on the image to be segmented.

[0035] (1a) Input the image to be segmented, and use the simple linear iterative clustering SLIC algorithm to over-segment the input image to be segmented into N superpixels: S=[s 1 ,s i ...,s N ], s i Is the i-th super pixel, i=1,...,N;

[0036] (1b) For each superpixel s i , Extract the 9-dimensional color feature vector x from the three color feature spaces of RGB, HSV and CIELab i ∈R 9 .

[0037] Step 2. Build a background dictionary.

[0038] Based on the boundary prior information, the super pixels in the boundary area are more likely to be the background area super pixels. Therefore, the present invention uses the super pixels in the image boundary area to construct t...

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Abstract

The invention discloses a robust sparse representation and Laplace regular term-based salient object detection method, and mainly aims at solving the problem that the existing method cannot completely and consistently detect the salient objects in complicated images. The method comprises the following steps of: 1, segmenting an input image to obtain a superpixel set; 2, constructing a background dictionary by adoption of superpixels at a boundary region; 3, respectively restraining the consistency between representation coefficients and reconstruction errors in a robust sparse representation model by adoption of two Laplace regular terms, and obtaining a representation coefficient matrix and a reconstruction error matrix by utilizing a background dictionary solution model; 5, constructing salient factors by combining the representation coefficient matrix and the reconstruction error matrix, so as to obtain a superpixel-level saliency map; and 6, mapping the superpixel-level saliency map to obtain a pixel-level saliency map. Experiments indicate that the method has relatively good background suppression effect, is capable of completely detecting salient objects of images, and can be used for the salient object detection of complicated scene images.

Description

Technical field [0001] The invention relates to the field of image processing, in particular to a salient target detection method, which can be used for salient target detection in complex background images. technical background [0002] The salient target detection aims to detect a target in the scene that is significantly different from the surrounding area and attracts attention, and to separate the salient target from the background completely and consistently. As an important image processing method, salient target detection has been widely used in image processing fields such as image segmentation, image restoration, and target recognition. [0003] Image salient object detection based on sparse representation is an important method of salient object detection. This type of method first over-segments the input image into several image blocks or super pixels; then, builds an over-complete dictionary, and performs sparse reconstruction of the image blocks or super pixels; fina...

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06T7/11G06K9/62
CPCG06T7/11G06F18/2136G06F18/22
Inventor 张强刘毅关永强霍臻王龙
Owner 重庆江雪科技有限公司
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