Content-Based Image Retrieval at the End of the Early Years
IEEE Transactions on Pattern Analysis and Machine Intelligence
Distinctive Image Features from Scale-Invariant Keypoints
International Journal of Computer Vision
Car Detection Based on Multi-Cues Integration
ICPR '04 Proceedings of the Pattern Recognition, 17th International Conference on (ICPR'04) Volume 2 - Volume 02
Histograms of Oriented Gradients for Human Detection
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 1 - Volume 01
Beyond Bags of Features: Spatial Pyramid Matching for Recognizing Natural Scene Categories
CVPR '06 Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 2
Canonical image selection from the web
Proceedings of the 6th ACM international conference on Image and video retrieval
Integrated Edge and Corner Detection
ICIAP '07 Proceedings of the 14th International Conference on Image Analysis and Processing
Speeded-Up Robust Features (SURF)
Computer Vision and Image Understanding
Learning color names for real-world applications
IEEE Transactions on Image Processing
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This research focuses on developing a system that can retrieve objects from a large image database by exploring the different types of image features. We propose a global representation of object based on the combination of multiple features. After that, we design a novel method for generic object detecting in still images with automatic feature selection. Our method is simple, computationally efficient The main advantage of this method is that it can automatically choose features which are the most suitale for detecting one type of object. We present experimental results for detecting many visual categories including side view car, front view car, bike, motorbike, train, aero plane, horse, sheep, flower and tower. Results clearly demonstrate that the proposed method is robust and produces good detection accuracy rate.