Uncovering shared structures in multiclass classification
Proceedings of the 24th international conference on Machine learning
The feature and spatial covariant kernel: adding implicit spatial constraints to histogram
Proceedings of the 6th ACM international conference on Image and video retrieval
Basic Image Features (BIFs) Arising from Approximate Symmetry Type
SSVM '09 Proceedings of the Second International Conference on Scale Space and Variational Methods in Computer Vision
Effective annotation and search for video blogs with integration of context and content analysis
IEEE Transactions on Multimedia - Special issue on integration of context and content
Object identification with tactile sensors using bag-of-features
IROS'09 Proceedings of the 2009 IEEE/RSJ international conference on Intelligent robots and systems
Learning natural scene categories by selective multi-scale feature extraction
Image and Vision Computing
Classifier fusion for SVM-based multimedia semantic indexing
ECIR'07 Proceedings of the 29th European conference on IR research
Using Basic Image Features for Texture Classification
International Journal of Computer Vision
Radial edge configuration for semi-local image structure description
ISVC'07 Proceedings of the 3rd international conference on Advances in visual computing - Volume Part I
Constructing vocabulary ensembles by different clustering algorithms for object categorization
IITA'09 Proceedings of the 3rd international conference on Intelligent information technology application
Dictionary learning based object detection and counting in traffic scenes
Proceedings of the Fourth ACM/IEEE International Conference on Distributed Smart Cameras
Spatial codebooks for image categorization
Proceedings of the 1st ACM International Conference on Multimedia Retrieval
Superpixel-Based interest points for effective bags of visual words medical image retrieval
MCBR-CDS'11 Proceedings of the Second MICCAI international conference on Medical Content-Based Retrieval for Clinical Decision Support
Self-similarity and points of interest in textured images
PerMIn'12 Proceedings of the First Indo-Japan conference on Perception and Machine Intelligence
Improving bag-of-visual-words model with spatial-temporal correlation for video retrieval
Proceedings of the 21st ACM international conference on Information and knowledge management
Continuous rotation invariant local descriptors for texton dictionary-based texture classification
Computer Vision and Image Understanding
Pairwise rotation invariant co-occurrence local binary pattern
ECCV'12 Proceedings of the 12th European conference on Computer Vision - Volume Part VI
Exploring bag of words architectures in the facial expression domain
ECCV'12 Proceedings of the 12th international conference on Computer Vision - Volume 2
Efficient development of user-defined image recognition systems
ACCV'12 Proceedings of the 11th international conference on Computer Vision - Volume Part I
Vehicle re-identification collaborating visual and temporal-spatial network
Proceedings of the Fifth International Conference on Internet Multimedia Computing and Service
Texture databases - A comprehensive survey
Pattern Recognition Letters
Robust human action recognition scheme based on high-level feature fusion
Multimedia Tools and Applications
| Hi-index | 0.00 |
Recently, methods based on local image features have shown promise for texture and object recognition tasks. This paper presents a large-scale evaluation of an approach that represents images as distributions (signatures or histograms) of features extracted from a sparse set of keypoint locations and learns a Support Vector Machine classifier with kernels based on two effective measures for comparing distributions, the Earth Mover's Distance and the 梅2 distance. We first evaluate the performance of our approach with different keypoint detectors and descriptors, as well as different kernels and classifiers. We then conduct a comparative evaluation with several state-of-the-art recognition methods on 4 texture and 5 object databases. On most of these databases, our implementation exceeds the best reported results and achieves comparable performance on the rest. Finally, we investigate the influence of background correlations on recognition performance.