The nature of statistical learning theory
The nature of statistical learning theory
Local Grayvalue Invariants for Image Retrieval
IEEE Transactions on Pattern Analysis and Machine Intelligence
Robust classification of arbitrary object classes based on hierarchical spatial feature-matching
Machine Vision and Applications
Making large-scale support vector machine learning practical
Advances in kernel methods
Histogram clustering for unsupervised segmentation and image retrieval
Pattern Recognition Letters
Recognition without Correspondence using MultidimensionalReceptive Field Histograms
International Journal of Computer Vision
Saliency, Scale and Image Description
International Journal of Computer Vision
Recognizing Surfaces Using Three-Dimensional Textons
ICCV '99 Proceedings of the International Conference on Computer Vision-Volume 2 - Volume 2
The Journal of Machine Learning Research
Affine-Invariant Local Descriptors and Neighborhood Statistics for Texture Recognition
ICCV '03 Proceedings of the Ninth IEEE International Conference on Computer Vision - Volume 2
Weakly Supervised Learning of Visual Models and Its Application to Content-Based Retrieval
International Journal of Computer Vision - Special Issue on Content-Based Image Retrieval
Distinctive Image Features from Scale-Invariant Keypoints
International Journal of Computer Vision
GaP: a factor model for discrete data
Proceedings of the 27th annual international ACM SIGIR conference on Research and development in information retrieval
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
Object Recognition with Features Inspired by Visual Cortex
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 2 - Volume 02
A Performance Evaluation of Local Descriptors
IEEE Transactions on Pattern Analysis and Machine Intelligence
Feature Hierarchies for Object Classification
ICCV '05 Proceedings of the Tenth IEEE International Conference on Computer Vision (ICCV'05) Volume 1 - Volume 01
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 Comparison of Affine Region Detectors
International Journal of Computer Vision
Multiclass Object Recognition with Sparse, Localized Features
CVPR '06 Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 1
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
Learning methods for generic object recognition with invariance to pose and lighting
CVPR'04 Proceedings of the 2004 IEEE computer society conference on Computer vision and pattern recognition
Recognizing objects in adversarial clutter: breaking a visual captcha
CVPR'03 Proceedings of the 2003 IEEE computer society conference on Computer vision and pattern recognition
Probabilistic latent semantic analysis
UAI'99 Proceedings of the Fifteenth conference on Uncertainty in artificial intelligence
Hyperfeatures – multilevel local coding for visual recognition
ECCV'06 Proceedings of the 9th European conference on Computer Vision - Volume Part I
Comparing compact codebooks for visual categorization
Computer Vision and Image Understanding
A BOVW based query generative model
MMM'11 Proceedings of the 17th international conference on Advances in multimedia modeling - Volume Part I
Efficient and Effective Visual Codebook Generation Using Additive Kernels
The Journal of Machine Learning Research
Computer Vision and Image Understanding
Image classification using probability higher-order local auto-correlations
ACCV'09 Proceedings of the 9th Asian conference on Computer Vision - Volume Part III
Topic based pose relevance learning in dance archives
Proceedings of the 21st ACM international conference on Information and knowledge management
Effective use of frequent itemset mining for image classification
ECCV'12 Proceedings of the 12th European conference on Computer Vision - Volume Part I
ECCV'12 Proceedings of the 12th European conference on Computer Vision - Volume Part IV
Learning hierarchical bag of words using naive bayes clustering
ACCV'12 Proceedings of the 11th Asian conference on Computer Vision - Volume Part I
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Histograms of local appearance descriptors are a popular representation for visual recognition. They are highly discriminant with good resistance to local occlusions and to geometric and photometric variations, but they are not able to exploit spatial co-occurrence statistics over scales larger than the local input patches. We present a multilevel visual representation that remedies this. The starting point is the notion that to detect object parts in images, in practice it often suffices to detect co-occurrences of more local object fragments. This can be formalized by coding image patches against a codebook of known fragments or a more general statistical model and locally histogramming the resulting labels to capture their co-occurrence statistics. Local patch descriptors are converted into somewhat less local histograms over label occurrences. The histograms are themselves local descriptor vectors so the process can be iterated to code ever larger assemblies of object parts and increasingly abstract or `semantic' image properties. We call these higher-level descriptors "hyperfeatures". We formulate the hyperfeature model and study its performance under several different image coding methods including k-means based Vector Quantization, Gaussian Mixtures, and combinations of these with Latent Dirichlet Allocation. We find that the resulting high-level features provide improved performance in several object image and texture image classification tasks.