Image classification and querying using composite region templates
Computer Vision and Image Understanding - Special issue on content-based access for image and video libraries
Content-Based Image Retrieval at the End of the Early Years
IEEE Transactions on Pattern Analysis and Machine Intelligence
Unsupervised Segmentation of Color-Texture Regions in Images and Video
IEEE Transactions on Pattern Analysis and Machine Intelligence
SIMPLIcity: Semantics-Sensitive Integrated Matching for Picture LIbraries
IEEE Transactions on Pattern Analysis and Machine Intelligence
Blobworld: Image Segmentation Using Expectation-Maximization and Its Application to Image Querying
IEEE Transactions on Pattern Analysis and Machine Intelligence
Learning and inferring a semantic space from user's relevance feedback for image retrieval
Proceedings of the tenth ACM international conference on Multimedia
Image Retrieval Based on Regions of Interest
IEEE Transactions on Knowledge and Data Engineering
A Bayesian Approach to Unsupervised One-Shot Learning of Object Categories
ICCV '03 Proceedings of the Ninth IEEE International Conference on Computer Vision - Volume 2
On image auto-annotation with latent space models
MULTIMEDIA '03 Proceedings of the eleventh ACM international conference on Multimedia
Robust Real-Time Face Detection
International Journal of Computer Vision
Distinctive Image Features from Scale-Invariant Keypoints
International Journal of Computer Vision
PLSA-based image auto-annotation: constraining the latent space
Proceedings of the 12th annual ACM international conference on Multimedia
Multi-level annotation of natural scenes using dominant image components and semantic concepts
Proceedings of the 12th annual ACM international conference on Multimedia
Sharing features: efficient boosting procedures for multiclass object detection
CVPR'04 Proceedings of the 2004 IEEE computer society conference on Computer vision and pattern recognition
Image classification for content-based indexing
IEEE Transactions on Image Processing
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Robust detection of a large dictionary of salient objects in natural image database is of fundamental importance to image retrieval systems. We review three popular frameworks for salient object detection, i.e., segmentation-based method, grid-based method and part-based method and discuss their advantages and limitations. We argue that using these frameworks individually is generally not enough to handle a large number of salient object classes accurately because of the intrinsic diversity of salient object features. Motivated by this observation, we have proposed a new system which combines the merits of these frameworks into one single hybrid system. The system automatically selects the appropriate modeling method for each individual object class using J measure and shape variance. We conduct comparison experiments on two popular image dataset -- Corel and LabelMe. Empirical results have shown that the proposed hybrid method is more general and can handle much more salient object classes in a robust manner.