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
A Performance Evaluation of Local Descriptors
IEEE Transactions on Pattern Analysis and Machine Intelligence
A Comparison of Affine Region Detectors
International Journal of Computer Vision
Groups of Adjacent Contour Segments for Object Detection
IEEE Transactions on Pattern Analysis and Machine Intelligence
Object fingerprints for content analysis with applications to street landmark localization
MM '08 Proceedings of the 16th ACM international conference on Multimedia
Space-time tradeoffs for approximate nearest neighbor searching
Journal of the ACM (JACM)
Query expansion for hash-based image object retrieval
MM '09 Proceedings of the 17th ACM international conference on Multimedia
ASIFT: A New Framework for Fully Affine Invariant Image Comparison
SIAM Journal on Imaging Sciences
Logo detection based on spatial-spectral saliency and partial spatial context
ICME'09 Proceedings of the 2009 IEEE international conference on Multimedia and Expo
SURF: speeded up robust features
ECCV'06 Proceedings of the 9th European conference on Computer Vision - Volume Part I
A New Approach to Image Copy Detection Based on Extended Feature Sets
IEEE Transactions on Image Processing
Accurate off-line query expansion for large-scale mobile visual search
Signal Processing
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Generating better object model from automatic expanded samples is an effective approach to improve the performance of object detection. However, most existing methods either don't work well with limited relevance images in corpus, or result in redundant features and the decrease of detection speed. In this paper, we propose a novel method called Affine Stable Characteristic to generate an object feature model using only one object sample. By integrating affine simulation with stable characteristic mining, a compact and informative object model is generated with high robustness to viewpoint and scale transformations. For characteristic mining, two new notions, Global Stability and Local Stability, are introduced to calculate the robustness of each object feature from complementary hierarchies. And they are combined to generate the final object feature model. Experiments show that our novel method is capable of detecting objects in various geometric and photometric transformations, while only acquiring one sample image. In a compiled dataset composed of many famous test sets, the detection accuracy can be improved 35.8% compared with traditional methods at rapid on-line speed. The proposed approach can also be well generalized to other content analysis tasks.