Visual Recognition and Categorization on the Basis of Similarities to Multiple Class Prototypes
International Journal of Computer Vision
Riemannian geometry and statistical machine learning
Riemannian geometry and statistical machine learning
FPGA architecture for fast parallel computation of co-occurrence matrices
Microprocessors & Microsystems
Gabor wavelets and General Discriminant Analysis for face identification and verification
Image and Vision Computing
Gabor wavelets and General Discriminant Analysis for face identification and verification
Image and Vision Computing
An Integrated Color and Intensity Co-occurrence Matrix
Pattern Recognition Letters
Combining color and spatial information for object recognition across illumination changes
Pattern Recognition Letters
2D Gaborface representation method for face recognition with ensemble and multichannel model
Image and Vision Computing
Image and Vision Computing
Accurate and Efficient Computation of Gabor Features in Real-Time Applications
ISVC '09 Proceedings of the 5th International Symposium on Advances in Visual Computing: Part I
Selecting, Optimizing and Fusing `Salient' Gabor Features for Facial Expression Recognition
ICONIP '09 Proceedings of the 16th International Conference on Neural Information Processing: Part I
A computational approach to fisher information geometry with applications to image analysis
EMMCVPR'05 Proceedings of the 5th international conference on Energy Minimization Methods in Computer Vision and Pattern Recognition
IEEE Transactions on Image Processing
The combination of differential evolution and color attention for object recognition
Proceedings of the Fifth International Conference on Internet Multimedia Computing and Service
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We present an object recognition approach using co-occurrence similarities of Gabor magnitude textures in this paper. A novel image descriptor, multichannel Gabor magnitude co-occurrence matrices (MGMCMs), is designed to characterize Gabor textures for object representation and similarity matching. The descriptor is a generalization of multichannel color co-occurrence matrices (MCMs), which focus on using robust and discriminative magnitude textures in filtered images. Our approach starts from Gabor wavelet transformation of each object image. An exploratory learning algorithm is proposed for learning channel-adaptive magnitude truncation parameters and level parameters. This allows us to design the magnitude quantization that can reduce overall biased and peaked levels of resulting feature distributions in each channel, to avoid over-sparse co-occurrence distributions on average. The direction-based grouping is adopted for computational complexity reduction of MGMCMs extraction under a specific neighborhood mode on the grouped rescaled magnitude images of per object image. When each MGMCM is treated as a probability distribution lying on a multinomial manifold, we represent per object image as a point on a product multinomial manifold. Using multinomial geometry and metric extension technique, we construct the p-order Minkowski co-occurrence information distance for similarity matching between the albums of Gabor magnitude textures. The feasibility and effectiveness of the approach is validated by the experimental results on the Yale and FERET face databases, PolyU palmprint database, COIL-20 object database and Zurich buildings database.