Saliency Detection using Horizontal and Vertical Color Differences
Date Issued
2014
Date
2014
Author(s)
Yang, Hao-Ming
Abstract
In computer vision, saliency detection of image is a fundamental problem, it’s helpful for applications like object segmentation, adaptive compression, and object recognition. Previous works demonstrate that distinctness is the dominating factor, and these works try to use various algorithms to compute distinctness, low-level cues like color, orientation, and pattern or high-level cues like face prior. This thesis proposed a new low-level feature to assist the detection. First, we use image segmentation to segment the image into homogeneous regions then extract global and local color features and combined with background prior. Second, the particularity of color on horizontal direction and vertical direction are calculated. In addition, we create connected-component to emphasis the completeness of an object. Finally, we obtain high-level cue of face prior by face detection and then integrate these features to generate the detected result. The experiment results show that the proposed method can find the region and object with higher saliency, and retain the completeness inside of the object.
Subjects
顏色特徵
水平與垂直特徵
特徵整合
Type
thesis
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