Remarkable object detecting method utilizing image boundary information and area connectivity

Abstract

The invention provides a remarkable object detecting method utilizing image boundary information and area connectivity. According to the method, superpixel segmenting is carried out on an image to be detected, average Lab color feature vectors of superpixels and the space topological relation of the superpixels are utilized for constructing three non-vector weight graphs, the shortest path from each superpixel to the image boundary is calculated to obtain three saliency maps, the three saliency maps are multiplied to obtain a final saliency map, and detection of salient objects is finished; local context information of the superpixels is utilized for correcting saliency values, so that the detection precision of the salient objects is improved, and then the saliency of a background region is reduced; in addition, a logistic regression device is adopted for carrying out feature integration on the corrected saliency map obtained through calculation carried out according to different kinds of connectivity to obtain final uniform and highlight saliency map in the saliency object region. According to the method, the saliency object region can be made highlight fast, and the false drop rate of a high-contrast region in the background can be reduced.

Description

technical field

[0001] The invention belongs to the technical field of image retrieval and image recognition, and relates to a salient object detection method, in particular to a new salient object detection method using image boundary information and image background connectivity prior. Background technique

[0002] An extraordinary ability of the human visual system is visual attention, that is, it can quickly select important information from complex scenes for further processing, while ignoring other information. The purpose of salient object detection is to generate a saliency map by modeling to predict the most attractive object in the image when people watch the image. The brightness of each pixel in the saliency map represents the saliency value at that place. The saliency value The larger the value, the greater the possibility of people's attention here. Salient object detection is a fundamental problem in computer vision and has broad application prospects in area...

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