A Computational Approach to Edge Detection
IEEE Transactions on Pattern Analysis and Machine Intelligence
A resource-allocating network for function interpolation
Neural Computation
C4.5: programs for machine learning
C4.5: programs for machine learning
ACM Computing Surveys (CSUR)
Mean Shift: A Robust Approach Toward Feature Space Analysis
IEEE Transactions on Pattern Analysis and Machine Intelligence
Saliency, Scale and Image Description
International Journal of Computer Vision
Machine Learning
An Affine Invariant Interest Point Detector
ECCV '02 Proceedings of the 7th European Conference on Computer Vision-Part I
Text Categorization with Suport Vector Machines: Learning with Many Relevant Features
ECML '98 Proceedings of the 10th European Conference on Machine Learning
A Novel Hierarchical Approach to Image Retrieval Using Color and Spatial Information
PCM '02 Proceedings of the Third IEEE Pacific Rim Conference on Multimedia: Advances in Multimedia Information Processing
Object Recognition from Local Scale-Invariant Features
ICCV '99 Proceedings of the International Conference on Computer Vision-Volume 2 - Volume 2
A Metric for Distributions with Applications to Image Databases
ICCV '98 Proceedings of the Sixth International Conference on Computer Vision
The Journal of Machine Learning Research
Video Google: A Text Retrieval Approach to Object Matching in Videos
ICCV '03 Proceedings of the Ninth IEEE International Conference on Computer Vision - Volume 2
Distinctive Image Features from Scale-Invariant Keypoints
International Journal of Computer Vision
Learning to Detect Objects in Images via a Sparse, Part-Based Representation
IEEE Transactions on Pattern Analysis and Machine Intelligence
A Bayesian Hierarchical Model for Learning Natural Scene Categories
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 2 - Volume 02
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
Creating Efficient Codebooks for Visual Recognition
ICCV '05 Proceedings of the Tenth IEEE International Conference on Computer Vision (ICCV'05) Volume 1 - Volume 01
A Maximum Entropy Framework for Part-Based Texture and Object Recognition
ICCV '05 Proceedings of the Tenth IEEE International Conference on Computer Vision (ICCV'05) Volume 1 - Volume 01
The Pyramid Match Kernel: Discriminative Classification with Sets of Image Features
ICCV '05 Proceedings of the Tenth IEEE International Conference on Computer Vision - Volume 2
Object Categorization by Learned Universal Visual Dictionary
ICCV '05 Proceedings of the Tenth IEEE International Conference on Computer Vision - Volume 2
Multiple Object Class Detection with a Generative Model
CVPR '06 Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 1
Scalable Recognition with a Vocabulary Tree
CVPR '06 Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 2
Beyond Bags of Features: Spatial Pyramid Matching for Recognizing Natural Scene Categories
CVPR '06 Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 2
International Journal of Computer Vision
Object Categorization Robust to Surface Markings using Entropy-guided Codebook
WACV '07 Proceedings of the Eighth IEEE Workshop on Applications of Computer Vision
Describing Visual Scenes Using Transformed Objects and Parts
International Journal of Computer Vision
Robust Object Detection with Interleaved Categorization and Segmentation
International Journal of Computer Vision
Speeded-Up Robust Features (SURF)
Computer Vision and Image Understanding
Universal and Adapted Vocabularies for Generic Visual Categorization
IEEE Transactions on Pattern Analysis and Machine Intelligence
Adaptive traitor tracing for large anonymous attack
Proceedings of the 8th ACM workshop on Digital rights management
Local Invariant Feature Detectors: A Survey
Local Invariant Feature Detectors: A Survey
Learning Optimal Compact Codebook for Efficient Object Categorization
WACV '08 Proceedings of the 2008 IEEE Workshop on Applications of Computer Vision
Learning non-redundant codebooks for classifying complex objects
ICML '09 Proceedings of the 26th Annual International Conference on Machine Learning
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
Hyperfeatures – multilevel local coding for visual recognition
ECCV'06 Proceedings of the 9th European conference on Computer Vision - Volume Part I
Biologically motivated perceptual feature: generalized robust invariant feature
ACCV'06 Proceedings of the 7th Asian conference on Computer Vision - Volume Part II
IEEE Transactions on Pattern Analysis and Machine Intelligence
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The codebook model-based approach, while ignoring any structural aspect in vision, nonetheless provides state-of-the-art performances on current datasets. The key role of a visual codebook is to provide a way to map the low-level features into a fixed-length vector in histogram space to which standard classifiers can be directly applied. The discriminative power of such a visual codebook determines the quality of the codebook model, whereas the size of the codebook controls the complexity of the model. Thus, the construction of a codebook is an important step which is usually done by cluster analysis. However, clustering is a process that retains regions of high density in a distribution and it follows that the resulting codebook need not have discriminant properties. This is also recognised as a computational bottleneck of such systems. In our recent work, we proposed a resource-allocating codebook, to constructing a discriminant codebook in a one-pass design procedure that slightly outperforms more traditional approaches at drastically reduced computing times. In this review we survey several approaches that have been proposed over the last decade with their use of feature detectors, descriptors, codebook construction schemes, choice of classifiers in recognising objects, and datasets that were used in evaluating the proposed methods.