A Method of the Image Emotion Notation Based Fuzzy Reasoning

Abstract:

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In this paper, we firstly get the Regions of Interest (ROI) using the Eye tracker and divide every image into two regions including ROI and Non- Regions of Interest (Non-ROI). Secondly, we extract the features of the two regions including ROI and Non-ROI, and get the whole features including texture feature and color feature. Finally, a fuzzy inference network model for image emotion notation using neural logic network is presented. The model describes how to classify the images into different emotions, and neural logic network is used to classification. The learning method proposed is multi-featured, and it allows taking into account the possible predictive power of a simultaneously considered feature conjunction. On the other hand, the feature space partition allows a fuzzy representation of the features and data imprecision integration.

Info:

Periodical:

Advanced Materials Research (Volumes 179-180)

Edited by:

Garry Zhu

Pages:

226-232

DOI:

10.4028/www.scientific.net/AMR.179-180.226

Citation:

H. F. Li et al., "A Method of the Image Emotion Notation Based Fuzzy Reasoning", Advanced Materials Research, Vols. 179-180, pp. 226-232, 2011

Online since:

January 2011

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Price:

$35.00

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