Feature Detection with Automatic Scale Selection
International Journal of Computer Vision
Mean Shift: A Robust Approach Toward Feature Space Analysis
IEEE Transactions on Pattern Analysis and Machine Intelligence
Shape Matching and Object Recognition Using Shape Contexts
IEEE Transactions on Pattern Analysis and Machine Intelligence
A Performance Evaluation of Local Descriptors
IEEE Transactions on Pattern Analysis and Machine Intelligence
Discovering Objects and their Localization in Images
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
Discriminative Object Class Models of Appearance and Shape by Correlatons
CVPR '06 Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 2
Groups of Adjacent Contour Segments for Object Detection
IEEE Transactions on Pattern Analysis and Machine Intelligence
Image annotation by modeling Supporting Region Graph
Applied Intelligence
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Bag of Words model has been widely used in the task of Object Categorization, and SIFT, computed for interest local regions, has been extracted from the image as the representative features, which can provide robustness and invariance to many kind of image transformation. Even though, they can only capture the local information, while be blind to the large picture of the image. Besides, the same part of different objects(like the head lamp of different cars) may also not able to be identically represented by SIFT and the like. In order to efficiently represent the object category, we design a new local descriptor---structural context, which shares the similar idea as Shape Context, capturing the relationship between current point and the remaining points, which is the extrema from the scale space of the image and can to some extent represent the structural of the image. This newly proposed descriptor can provide more discriminative representation of the object category, being invariant to intra-class difference, scale change, illumination variation, clutter noise, partial occlusion, small range of deformation, rotation and viewpoint change. Experiments on object categorization and image matching have proved the effectiveness of our newly proposed descriptor in describing the images of the same category.