An introduction to support Vector Machines: and other kernel-based learning methods
An introduction to support Vector Machines: and other kernel-based learning methods
Affine/ Photometric Invariants for Planar Intensity Patterns
ECCV '96 Proceedings of the 4th European Conference on Computer Vision-Volume I - Volume I
Multi-view Matching for Unordered Image Sets, or "How Do I Organize My Holiday Snaps?"
ECCV '02 Proceedings of the 7th European Conference on Computer Vision-Part I
Matching Widely Separated Views Based on Affine Invariant Regions
International Journal of Computer Vision
Distinctive Image Features from Scale-Invariant Keypoints
International Journal of Computer Vision
A Sparse Texture Representation Using Local Affine Regions
IEEE Transactions on Pattern Analysis and Machine Intelligence
A Performance Evaluation of Local Descriptors
IEEE Transactions on Pattern Analysis and Machine Intelligence
Deformation Invariant Image Matching
ICCV '05 Proceedings of the Tenth IEEE International Conference on Computer Vision - Volume 2
A Comparison of Affine Region Detectors
International Journal of Computer Vision
Local binary patterns for a hybrid fingerprint matcher
Pattern Recognition
Blur Insensitive Texture Classification Using Local Phase Quantization
ICISP '08 Proceedings of the 3rd international conference on Image and Signal Processing
Rotationally Invariant Hashing of Median Binary Patterns for Texture Classification
ICIAR '08 Proceedings of the 5th international conference on Image Analysis and Recognition
Fuzzy Local Binary Patterns for Ultrasound Texture Characterization
ICIAR '08 Proceedings of the 5th international conference on Image Analysis and Recognition
Description of interest regions with local binary patterns
Pattern Recognition
Rotation Invariant Image Description with Local Binary Pattern Histogram Fourier Features
SCIA '09 Proceedings of the 16th Scandinavian Conference on Image Analysis
Dominant local binary patterns for texture classification
IEEE Transactions on Image Processing
Enhanced local texture feature sets for face recognition under difficult lighting conditions
AMFG'07 Proceedings of the 3rd international conference on Analysis and modeling of faces and gestures
ACCV'07 Proceedings of the 8th Asian conference on Computer vision - Volume Part II
Local binary patterns variants as texture descriptors for medical image analysis
Artificial Intelligence in Medicine
PCA-SIFT: a more distinctive representation for local image descriptors
CVPR'04 Proceedings of the 2004 IEEE computer society conference on Computer vision and pattern recognition
SURF: speeded up robust features
ECCV'06 Proceedings of the 9th European conference on Computer Vision - Volume Part I
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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In this work we propose a novel method for object recognition based on a random selection of interest regions, texture features (local binary/ternary patterns and local phase quantization) for describing each region, a bag-of-features approach for describing each object, and classification using support vector machines (SVMs). In our approach, a set of features is extracted from each subwindow of the object image. These sets are quantified, and the resulting global descriptor vector is used as a characterization of the image (e.g., as a feature vector for learning an image classification rule based on a SVM classifier). The standard texture descriptor is not widely utilized in region description. One of the first texture descriptors explored in region description is the CS-LBP descriptor, where a local binary pattern (LBP) feature is used as the local feature in the SIFT method, the most well-known object recognition algorithm. Our approach based on texture descriptors is much simpler than the SIFT algorithm, yet it performs comparably well. Furthermore, we show that the fusion between our approach and SIFT obtains a very high AUC in the well-known PASCAL VOC2006 dataset.