Figure - available from: Applied Computational Intelligence and Soft Computing
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Four types of regions. (a) The red region denotes rectangle sample, (b) The blue regions denote neighborhoods of rectangle sample, (c) The blue regions denote boundaries of the image, (d) The blue regions denote image area except rectangle sample.
Source publication
The popularity of social networks has brought the rapid growth of social images which have become an increasingly important image type. One of the most obvious attributes of social images is the tag. However, the sate-of-the-art methods fail to fully exploit the tag information for saliency detection. Thus this paper focuses on salient region detec...
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Due to the rapid development of RGB-D sensors, increasing attention is being paid to depth image applications. Depth images play an important role in computer vision research. In this paper, we address the problem of inpainting for single depth images without corresponding color images as a guide. Within the framework of model-based optimization me...
Citations
... Jia et al. [18] proposed a visual saliency detection algorithm which incorporates both generative and discriminative saliency models into a unified framework, which generated a more continuous and smooth result. The author of [15] focused on salient region detection of social images using both image appearance features and image tag cues. Aksac et al. [2] proposed an efficient method for salient region detection, and the images were decomposed by using superpixel segmentation which groups similar pixels and generates compact regions. ...
Automated image annotation (AIA) is an important issue in computer vision and pattern recognition, and plays an extremely important role in retrieving large-scale images. In many image annotation approaches, different regions of the image are processed equally, which is inconsistent with the mechanism by which humans understand images. In order to improve the annotation performance of existing AIA approaches, a hybrid AIA approach based on visual attention mechanism (VAM) and the conditional random field (CRF) is proposed. First, since people pay more attention to the salient region of an image during the image recognition process, VAM is implemented for acquiring the salient and non salient regions of the image. Second, support vector machine (SVM) is used to annotate the salient region, and k nearest neighbor (kNN) voting algorithm is used to annotate the non salient regions. Finally, due to the existence of a certain relationship between any two annotation words (also called labels), CRF is calculated to obtain the final label set of each given image. The experimental results confirm that the proposed hybrid AIA approach has ideal annotation performance.
