International Journal of Computer Vision
Image Indexing Using Color Correlograms
CVPR '97 Proceedings of the 1997 Conference on Computer Vision and Pattern Recognition (CVPR '97)
Distinctive Image Features from Scale-Invariant Keypoints
International Journal of Computer Vision
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
ICCV '05 Proceedings of the Tenth IEEE International Conference on Computer Vision (ICCV'05) Volume 1 - Volume 01
A Visual Vocabulary for Flower Classification
CVPR '06 Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 2
LIBLINEAR: A Library for Large Linear Classification
The Journal of Machine Learning Research
Multiple Component Learning for Object Detection
ECCV '08 Proceedings of the 10th European Conference on Computer Vision: Part II
Automated Flower Classification over a Large Number of Classes
ICVGIP '08 Proceedings of the 2008 Sixth Indian Conference on Computer Vision, Graphics & Image Processing
Co-occurrence Histograms of Oriented Gradients for Pedestrian Detection
PSIVT '09 Proceedings of the 3rd Pacific Rim Symposium on Advances in Image and Video Technology
Cat face detection with two heterogeneous features
ICIP'09 Proceedings of the 16th IEEE international conference on Image processing
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Co-occurrence features are effective for object classification because observing co-occurrence of two events is far more informative than observing occurrence of each event separately. For example, a color co-occurrence histogram captures co-occurrence of pairs of colors at a given distance while a color histogram just expresses frequency of each color. As one of such co-occurrence features, CoHOG (co-occurrence histograms of oriented gradients) has been proposed and a method using CoHOG with a linear classifier has shown a comparable performance with state-of-the-art pedestrian detection methods. According to recent studies, it has been suggested that combining heterogeneous features such as texture, shape, and color is useful for object classification. Therefore, we introduce three heterogeneous features based on co-occurrence called color-CoHOG, CoHED, and CoHD, respectively. Each heterogeneous features are evaluated on the INRIA person dataset and the Oxford 17/102 category flower datasets. The experimental results show that color-CoHOG is effective for the INRIA person dataset and CoHED is effective for the Oxford flower datasets. By combining above heterogeneous features, the proposed method achieves comparable classification performance to state-of-the-art methods on the above datasets. The results suggest that the proposed method using heterogeneous features can be used as an off-the-shelf method for various object classification tasks.