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Image significance detection method based on improved graph model

A graphical model and salient technology, applied in the fields of image detection and computer vision, which can solve the problems of incomplete detection of salient objects and inability to uniformly highlight the interior of salient objects.

Active Publication Date: 2019-08-30
无锡尚合达智能科技有限公司
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Problems solved by technology

[0005] In view of the above problems, the present invention proposes an image saliency detection method based on an improved graph model to solve the problem of incomplete detection of salient objects in complex environments or the inability to uniformly highlight the inside of salient objects, and can completely detect and evenly segment the entire salient object. Improving the performance of image saliency detection algorithms will greatly promote the further research and development of image-related fields

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  • Image significance detection method based on improved graph model
  • Image significance detection method based on improved graph model
  • Image significance detection method based on improved graph model

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Embodiment 1

[0095] This embodiment provides an image saliency detection method based on an improved graph model, see figure 1 , the method includes the following steps:

[0096] Step 1. Segment the input image into N superpixels using Simple Linear Iterative Clustering (SLIC), and the i-th superpixel is denoted by v i Indicates that the jth superpixel is denoted by v j Indicates that i, j ∈ 1, 2, ..., N.

[0097] Step 2. Use the underlying features of the image to calculate the similarity between the superpixels to form a similarity matrix A=[a ij ] N×N , a ij Denotes superpixel v i and v j The degree of similarity, i, j∈1, 2,..., N;

[0098] (2.1) CLELAB color mean value c=(l, a, b) of the pixels included in the superpixel T To represent the color feature of the superpixel, the 59-dimensional vector t formed by the equivalent mode of the local binary pattern (LBP) represents the texture feature of the superpixel; the superpixel v i and v j The color feature distance Dc ij and ...

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Abstract

The invention discloses an image significance detection method based on an improved graph model, and belongs to the technical field of computer vision and image detection. The image significance detection method comprises the following steps: segmenting an image into superpixels by adopting simple linear iterative clustering, constructing an undirected graph by taking the superpixels as vertexes,and extracting high-level features by utilizing image bottom-level features and priori knowledge on the basis of an improved graph model to obtain a saliency map based on the bottom-level features; selecting foreground and background seed nodes by utilizing high-level features and compactness of a significant object, and respectively calculating and fusing a foreground seed-based saliency map anda background seed-based saliency map; and finally, fusing the saliency maps obtained in the two stages to obtain a final saliency map. According to the image significance detection method, the salientobjects in the image can be completely detected and uniformly highlighted; the detection accuracy of the salient objects in the complex environment is improved; the design requirement of an actual engineering system is met; and the problem that the detection accuracy of the salient objects in the complex environment is low is solved.

Description

technical field [0001] The invention relates to an image saliency detection method based on an improved graph model, and belongs to the technical fields of computer vision and image detection. Background technique [0002] Saliency detection aims to enable computers to have a human-like visual attention mechanism to find the most interesting and valuable information from complex scenes. The early saliency detection algorithm is aimed at the detection of visual attention, and the purpose is to predict the gaze point of the human eye in the image. Later, many salient region detections aimed at segmenting the entire salient object emerged. Compared with the former, salient region detection has higher application value. Saliency detection models can be divided into bottom-up and top-down categories. The bottom-up model is driven by data, using image color, contrast, etc. to calculate saliency; the top-down model is task-driven, and often needs to train a large number of sampl...

Claims

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Application Information

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IPC IPC(8): G06K9/34G06K9/46G06K9/62
CPCG06V10/267G06V10/462G06F18/23213G06F18/22
Inventor 葛洪伟张莹莹羊洁明江明
Owner 无锡尚合达智能科技有限公司
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