IEEE Transactions on Pattern Analysis and Machine Intelligence
Recognition without Correspondence using MultidimensionalReceptive Field Histograms
International Journal of Computer Vision
Digital Image Processing
Modeling the Shape of the Scene: A Holistic Representation of the Spatial Envelope
International Journal of Computer Vision
Distortion Invariant Object Recognition in the Dynamic Link Architecture
IEEE Transactions on Computers
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
Selection and Fusion of Color Models for Image Feature Detection
IEEE Transactions on Pattern Analysis and Machine Intelligence
Performance evaluation of local colour invariants
Computer Vision and Image Understanding
oRGB: A Practical Opponent Color Space for Computer Graphics
IEEE Computer Graphics and Applications
Learning the Uncorrelated, Independent, and Discriminating Color Spaces for Face Recognition
IEEE Transactions on Information Forensics and Security
Robust coding schemes for indexing and retrieval from large face databases
IEEE Transactions on Image Processing
IEEE Transactions on Image Processing
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This paper introduces several novel Gabor-based local, shape and color features for image classification. First, a new Gabor-HOG (GHOG) descriptor is proposed for image feature extraction by concatenating the Histograms of Oriented Gradients (HOG) of all the local Gabor filtered images. The GHOG descriptor is then further assessed in six different color spaces to measure classification performance. Finally, a novel Fused Color GHOG (FC-GHOG) feature is presented by integrating the PCA features of the six color GHOG descriptors that performs well on different object and scene image categories. The Enhanced Fisher Model (EFM) is applied for discriminatory feature extraction and the nearest neighbor classification rule is used for image classification. The robustness of the proposed GHOG and FC-GHOG feature vectors is evaluated using two grand challenge datasets, namely the Caltech 256 dataset and the MIT Scene dataset.