Significant object detection method based on sparse subspace clustering and low-order expression

Abstract

The invention discloses a significant object detection method based on sparse subspace clustering and low-order expression. The method comprises the steps of: 1, carrying out super pixel segmentation and clustering on an input image; 2, extracting the color, texture and edge characteristics of each super pixel in clusters, and constructing cluster characteristic matrixes; 3, ranking all super pixel characteristics according to the magnitude of color contrast, and constructing a dictionary; according to the dictionary, constructing a combined low-order expression model, solving the model and decomposing the characteristic matrixes of the clusters so as to obtain low-order expression coefficients, and calculating significant factors of the clusters; and 5, mapping the significant value of each cluster into the input image according the spatial position, and obtaining a significant map of the input image. According to the invention, the significant objects relatively large in size in the image can be completely and consistently detected, the noise in a background is inhibited, and the robustness of significant object detection of the image with the complex background is improved. The significant object detection method is applicable to image segmentation, object identification, image restoration and self-adaptive image compression.

Description

technical field [0001] The invention belongs to the technical field of image processing, and in particular relates to a salient object detection method, which can be used for image segmentation, object recognition, image recovery and adaptive image compression. Background technique [0002] The goal of salient object detection is to detect the most attractive object regions in an image completely and consistently. In recent years, with the expansion of visual information network and the rapid growth of e-commerce industry, the importance of image salient object detection technology has become increasingly prominent. As a new high-dimensional data analysis and processing tool, low-rank matrix recovery technology can effectively discover its intrinsic low-dimensional space from high-dimensional observation samples polluted by strong noise or partially missing, and this technology has been widely used In computer vision, machine learning, statistical analysis and other fields,...

AI Technical Summary

Problems solved by technology

However, the disadvantage of this method is that it is difficult to detect the salient objects with large size completely and consistently.

Since the salient objects with larger sizes are no longer sparse, this method does not essentially solve the detection problem of larger salient objects

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