A Model of Saliency-Based Visual Attention for Rapid Scene Analysis
IEEE Transactions on Pattern Analysis and Machine Intelligence
Contrast-based image attention analysis by using fuzzy growing
MULTIMEDIA '03 Proceedings of the eleventh ACM international conference on Multimedia
Is bottom-up attention useful for object recognition?
CVPR'04 Proceedings of the 2004 IEEE computer society conference on Computer vision and pattern recognition
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The attentive region extraction is a challenging issue for semantic interpretation of image and video content. The successful attentive region extraction greatly facilitates image classification, adaptation, compression and retrieval. Different from the traditional visual attention detection models, we propose a new attentive region extraction method based on out-of-focus blurring (OFB) technique used by professional photographers. Firstly, we combine metadata in Exchangeable Image File Format (EXIF) with visual features to quickly select professional photographs from image database. After that, an algorithm is implemented to automatically extract the attentive region from these photographs. This algorithm measures the saliency for individual pixels based on edge distribution of the images. The experimental results on OFB images have proved that our approach is able to overcome the contrast map selection problem of traditional visual attention methods and extract the attentive region using OFB information. The attentive region generated by our algorithm has similar shape and size with the subject of photographs which is a useful information for searching and retrieving the high-level semantic meaningful objects.