Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/12705
Title: A semantic no-reference image sharpness metric based on top-down and bottom-up saliency map modeling
Authors: Zhong, S
Liu, Y 
Liu, Y
Chung, FL 
Keywords: Image quality assessment
No-reference
Top-down & bottom-up saliency map
Issue Date: 2010
Publisher: IEEE
Source: 2010 17th IEEE International Conference on Image Processing (ICIP), 26-29 September 2010, Hong Kong, p. 1553-1556 How to cite?
Abstract: This work presents a semantic level no-reference image sharpness/blurriness metric under the guidance of top-down & bottom-up saliency map, which is learned based on eye-tracking data by SVM. Unlike existing metrics focused on measuring the blurriness in vision level, our metric more concerns about the image content and human's intention. We integrate visual features, center priority, and semantic meaning from tag information to learn a top-down & bottom-up saliency model based on the eye-tracking data. Empirical validations on standard dataset demonstrate the effectiveness of the proposed model and metric.
URI: http://hdl.handle.net/10397/12705
ISBN: 978-1-4244-7992-4
978-1-4244-7993-1 (E-ISBN)
ISSN: 1522-4880
DOI: 10.1109/ICIP.2010.5653807
Appears in Collections:Conference Paper

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