The Earth Mover's Distance as a Metric for Image Retrieval
International Journal of Computer Vision
Saliency, Scale and Image Description
International Journal of Computer Vision
Object Recognition from Local Scale-Invariant Features
ICCV '99 Proceedings of the International Conference on Computer Vision-Volume 2 - Volume 2
Video Google: A Text Retrieval Approach to Object Matching in Videos
ICCV '03 Proceedings of the Ninth IEEE International Conference on Computer Vision - Volume 2
Scale & Affine Invariant Interest Point Detectors
International Journal of Computer Vision
Distinctive Image Features from Scale-Invariant Keypoints
International Journal of Computer Vision
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 1 - Volume 01
A Performance Evaluation of Local Descriptors
IEEE Transactions on Pattern Analysis and Machine Intelligence
Creating Efficient Codebooks for Visual Recognition
ICCV '05 Proceedings of the Tenth IEEE International Conference on Computer Vision (ICCV'05) Volume 1 - Volume 01
A Comparison of Affine Region Detectors
International Journal of Computer Vision
Scalable Recognition with a Vocabulary Tree
CVPR '06 Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 2
Spatial Weighting for Bag-of-Features
CVPR '06 Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 2
Sampling strategies for bag-of-features image classification
ECCV'06 Proceedings of the 9th European conference on Computer Vision - Volume Part IV
Matching point sets with respect to the earth mover’s distance
ESA'05 Proceedings of the 13th annual European conference on Algorithms
Learning compositional categorization models
ECCV'06 Proceedings of the 9th European conference on Computer Vision - Volume Part III
Image segmentation and analysis via multiscale gradient watershed hierarchies
IEEE Transactions on Image Processing
Embedding spatial information into image content description for scene retrieval
Pattern Recognition
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In this paper, we describe an approach for retrieving a ranked list of images containing the same object as the query. Images in the corpus are primitively represented by a set of salient region descriptors so that the retrieval approach can works despite of changes in scale, illumination, and partial occlusion. Based on the sparse frequency vector representation method, we have improved the bag-of-features method to integrate the spatial co-occurrence information into the image representation. Instead of using the spatial configuration as a further verification stage following a prior filter stage, our method integrates the spatial information into the image representation and merges two stages in order to speed up the retrieval performance. To efficiently use the spatial configurations of salient regions, we propose a practical method to explore the content of image and flexibly cluster the salient regions into groups of neighbours. And the combination of information from the local co-occurrence of salient regions with the sparse frequency vector representation method is a major contribution of our work. Experiment results on a ground-truth dataset and complexity comparison are provided to demonstrate the advantage of our way of using spatial information for object retrieval work.