Applications of spatial data structures: Computer graphics, image processing, and GIS
Applications of spatial data structures: Computer graphics, image processing, and GIS
Local Grayvalue Invariants for Image Retrieval
IEEE Transactions on Pattern Analysis and Machine Intelligence
Evaluation of Interest Point Detectors
International Journal of Computer Vision - Special issue on a special section on visual surveillance
SIMPLIcity: Semantics-Sensitive Integrated Matching for Picture LIbraries
IEEE Transactions on Pattern Analysis and Machine Intelligence
Comparing salient point detectors
Pattern Recognition Letters
Multiple-Instance Learning for Natural Scene Classification
ICML '98 Proceedings of the Fifteenth International Conference on Machine Learning
Content Based Image Retrieval Using Interest Points and Texture Features
ICPR '00 Proceedings of the International Conference on Pattern Recognition - Volume 4
A Study of Shape-Based Image Retrieval
ICDCSW '04 Proceedings of the 24th International Conference on Distributed Computing Systems Workshops - W7: EC (ICDCSW'04) - Volume 7
Distinctive Image Features from Scale-Invariant Keypoints
International Journal of Computer Vision
Colour and Texture Features for Content Based Image Retrieval
CGIV '06 Proceedings of the International Conference on Computer Graphics, Imaging and Visualisation
IDEAS '06 Proceedings of the 10th International Database Engineering and Applications Symposium
A survey of content-based image retrieval with high-level semantics
Pattern Recognition
The Pyramid Match Kernel: Efficient Learning with Sets of Features
The Journal of Machine Learning Research
Image retrieval: Ideas, influences, and trends of the new age
ACM Computing Surveys (CSUR)
Localized Content-Based Image Retrieval
IEEE Transactions on Pattern Analysis and Machine Intelligence
Content-based image retrieval using visually significant point features
Fuzzy Sets and Systems
PCA-SIFT: a more distinctive representation for local image descriptors
CVPR'04 Proceedings of the 2004 IEEE computer society conference on Computer vision and pattern recognition
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Salient point detection in images is very useful for image processing applications like image compression, object detection and object recognition. It is also frequently used to represent local properties of the image in content-based image retrieval (CBIR). Many research results focused on finding the most salient points in the image. However, the large number of salient points and continuous point sets are still the problems. Based on the saliency values from wavelet-based methods, this paper presents a hierarchical algorithm for selecting the most salient points such that they cannot only give a satisfying representation of an image, but also make the image retrieval systems more efficiently. Under a top-down approach from quadtree data structure, the algorithm keeps the most salient points in each quadrant according to the percentage of saliency values in the whole image. The performance of the proposed method was evaluated with the spreading measure and retrieval rate from a CBIR system. In this experiment, it shows that our method is robust and the extracted salient points provide efficient retrieval performance comparing with two wavelet-based point detectors.