Robust Object Recognition with Cortex-Like Mechanisms
IEEE Transactions on Pattern Analysis and Machine Intelligence
The feature and spatial covariant kernel: adding implicit spatial constraints to histogram
Proceedings of the 6th ACM international conference on Image and video retrieval
Information-theoretic semantic multimedia indexing
Proceedings of the 6th ACM international conference on Image and video retrieval
Unsupervised texture classification: Automatically discover and classify texture patterns
Image and Vision Computing
Nearest hyperdisk methods for high-dimensional classification
Proceedings of the 25th international conference on Machine learning
Latent mixture vocabularies for object categorization and segmentation
Image and Vision Computing
A Bag of Strings Representation for Image Categorization
Journal of Mathematical Imaging and Vision
Scale-invariant visual language modeling for object categorization
IEEE Transactions on Multimedia - Special issue on integration of context and content
Semi-latent Dirichlet allocation: a hierarchical model for human action recognition
Proceedings of the 2nd conference on Human motion: understanding, modeling, capture and animation
Quantization-based clustering algorithm
Pattern Recognition
An information-theoretic framework for semantic-multimedia retrieval
ACM Transactions on Information Systems (TOIS)
Building compact local pairwise codebook with joint feature space clustering
ECCV'10 Proceedings of the 11th European conference on Computer vision: Part I
Visual recognition with humans in the loop
ECCV'10 Proceedings of the 11th European conference on Computer vision: Part IV
Optimal operations for visual categorization
ICIMCS '10 Proceedings of the Second International Conference on Internet Multimedia Computing and Service
EURASIP Journal on Advances in Signal Processing - Special issue on biologically inspired signal processing: analyses, algorithms and applications
Towards a universal and limited visual vocabulary
ISVC'11 Proceedings of the 7th international conference on Advances in visual computing - Volume Part II
A Bayesian approach for object classification based on clusters of SIFT local features
Expert Systems with Applications: An International Journal
Inexact graph matching based on kernels for object retrieval in image databases
Image and Vision Computing
Accurate Object Recognition with Shape Masks
International Journal of Computer Vision
Incorporating spatial correlogram into bag-of-features model for scene categorization
ACCV'09 Proceedings of the 9th Asian conference on Computer Vision - Volume Part I
Coloring local feature extraction
ECCV'06 Proceedings of the 9th European conference on Computer Vision - Volume Part II
Visual query processing for efficient image retrieval using a SOM-based filter-refinement scheme
Information Sciences: an International Journal
A Review of Codebook Models in Patch-Based Visual Object Recognition
Journal of Signal Processing Systems
Image retrieval and annotation using maximum entropy
CLEF'06 Proceedings of the 7th international conference on Cross-Language Evaluation Forum: evaluation of multilingual and multi-modal information retrieval
Efficient similarity derived from kernel-based transition probability
ECCV'12 Proceedings of the 12th European conference on Computer Vision - Volume Part VI
Learning hierarchical bag of words using naive bayes clustering
ACCV'12 Proceedings of the 11th Asian conference on Computer Vision - Volume Part I
Undo the codebook bias by linear transformation for visual applications
Proceedings of the 21st ACM international conference on Multimedia
An experimental study on the universality of visual vocabularies
Journal of Visual Communication and Image Representation
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This paper presents a probabilistic part-based approach for texture and object recognition. Textures are represented using a part dictionary found by quantizing the appearance of scale- or affine-invariant keypoints. Object classes are represented using a dictionary of composite semi-local parts, or groups of neighboring keypoints with stable and distinctive appearance and geometric layout. A discriminative maximum entropy framework is used to learn the posterior distribution of the class label given the occurrences of parts from the dictionary in the training set. Experiments on two texture and two object databases demonstrate the effectiveness of this framework for visual classification.