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Your search keyword '"Li, Xuelong"' showing total 3 results
3 results on '"Li, Xuelong"'

1. Spatiochromatic Context Modeling for Color Saliency Analysis.

Author

Zhang, Jun, Wang, Meng, Zhang, Shengping, Li, Xuelong, and Wu, Xindong

Subjects
COGNITIVE psychology research, VISUAL cortex, COMPUTER vision, MATHEMATICAL models, PATTERN recognition systems, IMAGE processing
Abstract

Visual saliency is one of the most noteworthy perceptual abilities of human vision. Recent progress in cognitive psychology suggests that: 1) visual saliency analysis is mainly completed by the bottom-up mechanism consisting of feedforward low-level processing in primary visual cortex (area V1) and 2) color interacts with spatial cues and is influenced by the neighborhood context, and thus it plays an important role in a visual saliency analysis. From a computational perspective, the most existing saliency modeling approaches exploit multiple independent visual cues, irrespective of their interactions (or are not computed explicitly), and ignore contextual influences induced by neighboring colors. In addition, the use of color is often underestimated in the visual saliency analysis. In this paper, we propose a simple yet effective color saliency model that considers color as the only visual cue and mimics the color processing in V1. Our approach uses region-/boundary-defined color features with spatiochromatic filtering by considering local color-orientation interactions, therefore captures homogeneous color elements, subtle textures within the object and the overall salient object from the color image. To account for color contextual influences, we present a divisive normalization method for chromatic stimuli through the pooling of contrary/complementary color units. We further define a color perceptual metric over the entire scene to produce saliency maps for color regions and color boundaries individually. These maps are finally globally integrated into a one single saliency map. The final saliency map is produced by Gaussian blurring for robustness. We evaluate the proposed method on both synthetic stimuli and several benchmark saliency data sets from the visual saliency analysis to salient object detection. The experimental results demonstrate that the use of color as a unique visual cue achieves competitive results on par with or better than 12 state-of-the-art approaches. [ABSTRACT FROM PUBLISHER]

Published
2016
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2. Visual-Context Boosting for Eye Detection.

Author

Song, Mingli, Tao, Dacheng, Sun, Zhuo, and Li, Xuelong

Subjects
IMAGE processing, FEATURE extraction, PATTERN recognition systems, COMPUTER vision, EYE movements, IMAGE analysis
Abstract

Eye detection plays an important role in many practical applications. This paper presents a novel two-step scheme for eye detection. The first step models an eye by a newly defined visual-context pattern (VCP), and the second step applies semisupervised boosting for precise detection. VCP describes both the space and appearance relations between an eye region (region of eye) and a reference region (region of reference). The context feature of a VCP is extracted by using the integral image. Aiming to reduce the human labeling efforts, we apply semisupervised boosting, which integrates the context feature and the Haar-like features for precise eye detection. Experimental results on several standard face data sets demonstrate that the proposed approach is effective, robust, and efficient. We finally show that this approach is ready for practical applications. [ABSTRACT FROM AUTHOR]

Published
2010
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3. Multi-spectral saliency detection

Author

Wang, Qi, Yan, Pingkun, Yuan, Yuan, and Li, Xuelong

Subjects
*COMPUTER vision, *PATTERN recognition systems, *IMAGE converters, *ALGORITHMS, *FEATURE extraction, *COLOR
Abstract

Abstract: Visual saliency detection has been applied in many tasks in the fields of pattern recognition and computer vision, such as image segmentation, object recognition, and image retargeting. However, the accurate detection of saliency remains a challenge. The reasons behind this are that: (1) well-defined mechanism for saliency definition is rarely established; and (2) supporting information for detecting saliency is limited in general. In this paper, a multi-spectrum based saliency detection algorithm is proposed. Instead of only using the conventional RGB information as what existing algorithms do, this work incorporates near-infrared clues into the detection framework. Features of color and texture from both types of image modes are explored simultaneously. When calculating the color contrast, an effective color component analysis method is employed to produce more precise results. With respect to the texture analysis, texton representation is adopted for fast processing. Experiments are done to compare the proposed algorithm with other 11 state-of-the-art algorithms and the results indicate that our algorithm outperforms the others. [Copyright &y& Elsevier]

Published
2013
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