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Your search keyword '"Image texture"' showing total 2 results
2 results on '"Image texture"'

1. A No-Reference Texture Regularity Metric Based on Visual Saliency

Author

Srenivas Varadarajan and Lina J. Karam

Subjects
Texture compression, business.industry, Mean opinion score, ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION, Pattern recognition, Image processing, Computer Graphics and Computer-Aided Design, Visualization, Image texture, Texture filtering, Computer vision, Artificial intelligence, business, Image retrieval, Software, ComputingMethodologies_COMPUTERGRAPHICS, Texture synthesis, Mathematics
Abstract

This paper presents a no-reference perceptual metric that quantifies the degree of perceived regularity in textures. The metric is based on the similarity of visual attention (VA) of the textural primitives and the periodic spatial distribution of foveated fixation regions throughout the image. A ground-truth eye-tracking database for textures is also generated as part of this paper and is used to evaluate the performance of the most popular VA models. Using the saliency map generated by the best VA model, the proposed texture regularity metric is computed. It is shown through subjective testing that the proposed metric has a strong correlation with the mean opinion score for the perceived regularity of textures. The proposed texture regularity metric can be used to improve the quality and performance of many image processing applications like texture synthesis, texture compression, and content-based image retrieval.

Published
2015

2. A Co-Saliency Model of Image Pairs.

Author

Li, Hongliang and Ngan, King Ngi

Subjects
*IMAGE registration, *FEATURE extraction, *ALGORITHMS, *PYRAMIDS (Geometry), *IMAGE segmentation, *SIMILARITY (Geometry), *IMAGE processing, *PERFORMANCE evaluation
Abstract

In this paper, we introduce a method to detect co-saliency from an image pair that may have some objects in common. The co-saliency is modeled as a linear combination of the single-image saliency map (SISM) and the multi-image saliency map (MISM). The first term is designed to describe the local attention, which is computed by using three saliency detection techniques available in literature. To compute the MISM, a co-multilayer graph is constructed by dividing the image pair into a spatial pyramid representation. Each node in the graph is described by two types of visual descriptors, which are extracted from a representation of some aspects of local appearance, e.g., color and texture properties. In order to evaluate the similarity between two nodes, we employ a normalized single-pair SimRank algorithm to compute the similarity score. Experimental evaluation on a number of image pairs demonstrates the good performance of the proposed method on the co-saliency detection task. [ABSTRACT FROM AUTHOR]

Published
2011
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