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As one of the basic properties of image, texture undoubtedly affect the image saliency. We introduce a texture contrast based salient region detection method, which first divide an input image into several nearly uniform super pixels, then analyze the texture feature and calculate the texture differences between regions to detect salient region. In order to obtain a better saliency map, we also optimize...
Salient region detection is important for many computer vision tasks. The saliency detection results may serve as the basis for further high-level vision tasks like object segmentation and tracking. In this study, the authors propose an integration approach to detect salient region based on three principles from psychological evidence and observations of images, including colour contrast in a global...
Visual saliency is an important cue in human visual system, it can identify salient region in image. Image contrast has been utilized as an effective feature to detect the salient region. The conventional contrast measures utilize both spectral and spatial properties of image in many salient region detection methods. However, they only consider the local characteristics of image region, consequently,...
Salient region detection is to uniformly locate interest regions or objects in an image. It is a hot topic in computer vision, and has a wide range of applications like object recognition and segmentation. Although considerable progress has been made, salient region detection remains a challenging issue. In this paper we present a simple yet effective salient region detection approach by integrating...
Modeling visual attention is a challenging task for machine vision. In this paper, inspired by the mechanism of human visual system, we propose an integrated model to detect generic salient-regions in a purely bottom-up manner. Instead of only employing early visual features in most relevant works, the saliency of discriminative local regions is also conducted to represent the spatial entropy, which...
Object recognition based on probabilistic Latent Semantic Analysis (pLSA) has shown excellent performance, but it is sensitive to background clutter. In this paper, we propose a novel framework called AM-pLSA, which combines pLSA with visual attention model, to learn object classes from unlabeled images with cluttered background. We firstly detect salient regions and non-salient regions in an image...
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