A Model of Saliency-Based Visual Attention for Rapid Scene Analysis
IEEE Transactions on Pattern Analysis and Machine Intelligence
SVM-based Salient Region(s) Extraction Method for Image Retrieval
ICPR '04 Proceedings of the Pattern Recognition, 17th International Conference on (ICPR'04) Volume 2 - Volume 02
Learning in Region-Based Image Retrieval with Generalized Support Vector Machines
CVPRW '04 Proceedings of the 2004 Conference on Computer Vision and Pattern Recognition Workshop (CVPRW'04) Volume 9 - Volume 09
Statistical modeling and conceptualization of natural images
Pattern Recognition
A novel region-based image retrieval algorithm using selective visual attention model
ACIVS'05 Proceedings of the 7th international conference on Advanced Concepts for Intelligent Vision Systems
An efficient and effective region-based image retrieval framework
IEEE Transactions on Image Processing
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It has been shown that support vector machines (SVM) can be used in content-based image retrieval. Existing SVM based methods only extract low-level global or region-based features to form feature vectors and use traditional non-structured kernel function. However, these methods rarely consider the image structure or some new structured kernel types. In order to bridge the semantic gap between low-level features and high-level concepts, in this paper, a novel graph kernel based SVM method is proposed, which takes into account both low-level features and structural information of the image. Firstly, according to human selective visual attention model, for a given image, salient regions are extracted and the concept of Salient Region Adjacency Graph (SRAG) is proposed to represent the image semantics. Secondly, based on the SRAG, a novel graph kernel based SVM is constructed for image semantic retrieval. Experiments show that the proposed method shows better performance in image semantic retrieval than traditional method.