Feature Detection with Automatic Scale Selection
International Journal of Computer Vision
Evaluation of Interest Point Detectors
International Journal of Computer Vision - Special issue on a special section on visual surveillance
Modeling the Shape of the Scene: A Holistic Representation of the Spatial Envelope
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
Local invariant feature detectors: a survey
Foundations and Trends® in Computer Graphics and Vision
Evaluating Color Descriptors for Object and Scene Recognition
IEEE Transactions on Pattern Analysis and Machine Intelligence
Computer Vision: Algorithms and Applications
Computer Vision: Algorithms and Applications
Sampling strategies for bag-of-features image classification
ECCV'06 Proceedings of the 9th European conference on Computer Vision - Volume Part IV
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Detecting an interest point in the images to extract the features from it is an important step in many computer vision applications. For good performance, these points have to be robust against any transformation that can be done on the images such as viewpoint change, scaling change, rotation, and illumination and, etc. Many of the suggested interest point detectors are measuring the pixel-wise differences in the image intensity or image color. Lee and Chen [1] used image histogram representation instead of pixel representation to detect the interest points. They used the gradient histogram and the RGB color histogram representation. In this work, different color model's histogram representation such as Ohta-color histogram, HSV-color histogram, Opponent color histogram and Transformed-color histogram are implemented and used in the proposed interest point detector. These detectors are evaluated by measuring their repeatability and matching score between the detected points in the image matching task and the classification accuracy in the image classification task. It is found that as compared with intensity pixels detectors and Lee's histogram detectors, the proposed histogram detectors performed better under some image conditions such as illumination change, blur and some other conditions.