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
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 Comparison of Affine Region Detectors
International Journal of Computer Vision
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
Towards optimal bag-of-features for object categorization and semantic video retrieval
Proceedings of the 6th ACM international conference on Image and video retrieval
Multilevel Image Coding with Hyperfeatures
International Journal of Computer Vision
Probabilistic optimized ranking for multimedia semantic concept detection via RVM
CIVR '08 Proceedings of the 2008 international conference on Content-based image and video retrieval
Tracking by Hierarchical Representation of Target Structure
SSPR & SPR '08 Proceedings of the 2008 Joint IAPR International Workshop on Structural, Syntactic, and Statistical Pattern Recognition
Visual word proximity and linguistics for semantic video indexing and near-duplicate retrieval
Computer Vision and Image Understanding
Foreground Focus: Unsupervised Learning from Partially Matching Images
International Journal of Computer Vision
Sparse B-spline polynomial descriptors for human activity recognition
Image and Vision Computing
Scale-invariant visual language modeling for object categorization
IEEE Transactions on Multimedia - Special issue on integration of context and content
Silhouette representation and matching for 3D pose discrimination - A comparative study
Image and Vision Computing
Applying pLSA to region-based image categorization with soft vector quantization
Proceedings of the First International Conference on Internet Multimedia Computing and Service
Novel Gaussianized vector representation for improved natural scene categorization
Pattern Recognition Letters
Tutor-based learning of visual categories using different levels of supervision
Computer Vision and Image Understanding
Hierarchical learning of dominant constellations for object class recognition
ACCV'07 Proceedings of the 8th Asian conference on Computer vision - Volume Part I
Speeding up HOG and LBP features for pedestrian detection by multiresolution techniques
ISVC'10 Proceedings of the 6th international conference on Advances in visual computing - Volume Part I
3D human pose recovery from image by efficient visual feature selection
Computer Vision and Image Understanding
EURASIP Journal on Advances in Signal Processing - Special issue on biologically inspired signal processing: analyses, algorithms and applications
Sampling strategies for bag-of-features image classification
ECCV'06 Proceedings of the 9th European conference on Computer Vision - Volume Part IV
A Review of Codebook Models in Patch-Based Visual Object Recognition
Journal of Signal Processing Systems
Intelligent multi-camera video surveillance: A review
Pattern Recognition Letters
Approximate gaussian mixtures for large scale vocabularies
ECCV'12 Proceedings of the 12th European conference on Computer Vision - Volume Part III
Detecting regions from single scale edges
ECCV'10 Proceedings of the 11th European conference on Trends and Topics in Computer Vision - Volume Part I
Hierarchical object representations for visual recognition via weakly supervised learning
ACCV'12 Proceedings of the 11th Asian conference on Computer Vision - Volume Part I
Hi-index | 0.00 |
Histograms of local appearance descriptors are a popular representation for visual recognition. They are highly discriminant and have good resistance to local occlusions and to geometric and photometric variations, but they are not able to exploit spatial co-occurrence statistics at scales larger than their local input patches. We present a new multilevel visual representation, ‘hyperfeatures', that is designed to remedy this. The starting point is the familiar notion that to detect object parts, in practice it often suffices to detect co-occurrences of more local object fragments – a process that can be formalized as comparison (e.g. vector quantization) of image patches against a codebook of known fragments, followed by local aggregation of the resulting codebook membership vectors to detect co-occurrences. This process converts local collections of image descriptor vectors into somewhat less local histogram vectors – higher-level but spatially coarser descriptors. We observe that as the output is again a local descriptor vector, the process can be iterated, and that doing so captures and codes ever larger assemblies of object parts and increasingly abstract or ‘semantic' image properties. We formulate the hyperfeatures model and study its performance under several different image coding methods including clustering 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.