Significance detection method based on sparse expression and label propagation

A technique of sparse representation and label propagation, applied in image data processing, instrumentation, computing, etc.

Inactive Publication Date: 2016-12-14
ZHENGZHOU UNIVERSITY OF AERONAUTICS
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There have been many excellent algorithms in this field, but it i

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  • Significance detection method based on sparse expression and label propagation
  • Significance detection method based on sparse expression and label propagation
  • Significance detection method based on sparse expression and label propagation

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Embodiment Construction

[0055] 1. A saliency detection method based on sparse representation and label propagation, characterized in that: comprising the following steps:

[0056] Step 1: Build an adjacency matrix

[0057] Using the SLIC algorithm, the image is divided into N superpixels, this N superpixels N data, and then for this by N A data set composed of data is sparsely represented, and the sparse representation of each point in the data set is obtained by formula (1):

[0058] (1)

[0059] in yes N A data set consisting of superpixels, the optimal solution of formula (1) ;let the matrix for the dataset remove the first i List The resulting new matrix, , D is the data dimension, considering the influence of noise and the sensitivity of the signal to the overcomplete data matrix, the point relative to the matrix The sparse representation of is shown in formula (2):

[0060] (2)

[0061] in, , is a constant, for the first i feature vectors of superpi...

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Abstract

The invention provides a significance detection method based on sparse expression and label propagation. A new adjacent matrix is defined by use of the sparse expression, regions with common boundaries are called as adjacent regions and even data points in the same sub-space are defined as neighbors; then, a weight matrix is calculated through the similarity among regions in the image; parts of marginal regions are selected to be background labels; and label information of unlabeled regions is predicted through the weight matrix and the background labels obtained through the above method by applying a label propagation algorithm. The beneficial effects are that: by considering the relation between global information and local regions of the image, a new adjacent matrix is established; and by combining advantages of a sparse expression theory and the label propagation algorithm, the significance detection method has quite high accuracy and regression rate and is quite low in errors.

Description

technical field [0001] The invention relates to the field of image saliency detection, in particular to a saliency detection method based on sparse representation and label propagation. Background technique [0002] In recent years, saliency detection has become one of the hot topics in the field of computer vision, attracting the interest of a large number of scholars. Many excellent algorithms have emerged in this field, but it is still difficult to develop a simple and practical saliency model. At present, saliency detection has been widely used in related fields such as visual tracking, image classification and image segmentation. [0003] According to different detection models and functions, saliency detection algorithms can be divided into visual attention detection and salient object detection. Among them, visual attention detection is to estimate the change trajectory of the gaze point when the human eye observes an image, which has been widely studied in neurolog...

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Application Information

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IPC IPC(8): G06T7/00
CPCG06T7/0002
Inventor 张晓煜林晓刘喜玲王春香史军勇李玲玲刘丽
Owner ZHENGZHOU UNIVERSITY OF AERONAUTICS
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