An Image Saliency Detection Method Based on Manifold Regularized Support Vector Machine

Abstract

The invention provides an image saliency detection method based on a manifold regularization support vector machine, which belongs to the technical field of computer vision. The invention provides an image saliency detection method based on a semi-supervised manifold regularization support vector machine, which does not need artificially marked true values, only requires a small amount of training samples, and performs salient target detection from the perspectives of the global image and the local image respectively. . The method of the present invention uses a small number of pseudo-labeled samples for training, which reduces the manual workload and model training costs; constructs a manifold regular matrix from the perspectives of the global image and the local image respectively, ensuring the prominence and integrity of the detected salient objects; The joint optimization method further optimizes the saliency detection results predicted by the support vector machine model, making the detection more accurate and the target area brighter and smoother; Among them, the method of the present invention can also be detected better.

Description

technical field [0001] The invention belongs to the technical field of computer vision and relates to the technical field of image information processing, in particular to an image saliency detection method based on a manifold regularization support vector machine model, which is suitable for the salient target detection of static images. Background technique [0002] With the development of computer technology and the popularization of digital electronic products, image resources are becoming more and more abundant, which satisfies people's massive collection and application of image information, but the problem of complicated and redundant information also follows. Inspired by the efficient visual information processing mechanism of the biological vision system, image saliency detection in the field of computer vision came into being. The salient things in the image scene are basically rich in the main information of the image. More and more researchers have begun to explo...

AI Technical Summary

Problems solved by technology

[0003] Although great research results have been achieved in the field of image saliency detection, there are still many problems that have not been resolved, such as the image contains multiple salient objects, the object scale is too large or too small, and the detection results of some algorithms are not very accurate.

In addition, some saliency detection algorithms adopt a supervised method, which requires a large number of artificially labeled real-value training samples to train the detection model, which is relatively expensive; some saliency detection algorithms only use local or global image perspectives Performing saliency detection, resulting in incomplete or insufficiently prominent detected saliency targets

Images