A Model of Saliency-Based Visual Attention for Rapid Scene Analysis
IEEE Transactions on Pattern Analysis and Machine Intelligence
Content-Based Image Retrieval at the End of the Early Years
IEEE Transactions on Pattern Analysis and Machine Intelligence
Stylization and abstraction of photographs
Proceedings of the 29th annual conference on Computer graphics and interactive techniques
Attentional Selection for Object Recognition A Gentle Way
BMCV '02 Proceedings of the Second International Workshop on Biologically Motivated Computer Vision
Models of bottom-up and top-down visual attention
Models of bottom-up and top-down visual attention
Automatic thumbnail cropping and its effectiveness
Proceedings of the 16th annual ACM symposium on User interface software and technology
An attention-driven model for grouping similar images with image retrieval applications
EURASIP Journal on Applied Signal Processing
Is bottom-up attention useful for object recognition?
CVPR'04 Proceedings of the 2004 IEEE computer society conference on Computer vision and pattern recognition
Legible thumbnail: summarizing on-line handwritten documents based on emphasized expressions
Proceedings of the 13th International Conference on Human Computer Interaction with Mobile Devices and Services
Image retargeting using controlled shrinkage
Proceedings of the Eighth Indian Conference on Computer Vision, Graphics and Image Processing
Re-targeting of multi-script document images for handheld devices
Proceedings of the 4th International Workshop on Multilingual OCR
Neurocomputing
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Most of the Content-Based Image Retrieval (CBIR) solutions treat each image as a whole. But the fact that often a user will be searching for a part of the image i.e., a region in the image with obvious semantic meaning as opposed to the entire picture has led to view an image as a set of Regions of Interest (ROIs) rather than viewing it as a whole. To provide a quick scanning of large number of images thumbnail representation of the images are used in most of the image retrieval and browsing systems. Thumbnails generated by shrinking the original image often render the ROIs illegible. In this paper, we present an intelligent automatic cropping technique, prior to shrinking, based on an efficient, unsupervised visual attention driven ROI detection framework that can make ROIs of an image more recognizable. Experiments show that the thumbnails resulting from the proposed technique will efficiently increase the CBIR performance and is a valid approach to carry further research.