Weighted decomposition kernels
ICML '05 Proceedings of the 22nd international conference on Machine learning
The Pyramid Match Kernel: Efficient Learning with Sets of Features
The Journal of Machine Learning Research
Towards optimal bag-of-features for object categorization and semantic video retrieval
Proceedings of the 6th ACM international conference on Image and video retrieval
Linear-Time Computation of Similarity Measures for Sequential Data
The Journal of Machine Learning Research
Kernel Methods in Computer Vision
Foundations and Trends® in Computer Graphics and Vision
Spatio-temporal constraints for on-line 3D object recognition in videos
Computer Vision and Image Understanding
Large scale image clustering with support vector machine based on visual keywords
Proceedings of the Tenth International Workshop on Multimedia Data Mining
Data-driven suggestions for creativity support in 3D modeling
ACM SIGGRAPH Asia 2010 papers
A fast dual method for HIK SVM learning
ECCV'10 Proceedings of the 11th European conference on Computer vision: Part II
Novel kernel-based recognizers of human actions
EURASIP Journal on Advances in Signal Processing - Special issue on video analysis for human behavior understanding
Geometry aware local kernels for object recognition
ACCV'10 Proceedings of the 10th Asian conference on Computer vision - Volume Part I
View-tuned approximate partial matching kernel from hierarchical growing neural gases
ICANN'11 Proceedings of the 21st international conference on Artificial neural networks - Volume Part II
Efficient and Effective Visual Codebook Generation Using Additive Kernels
The Journal of Machine Learning Research
Analysis on a local approach to 3d object recognition
DAGM'06 Proceedings of the 28th conference on Pattern Recognition
Bag of spatio-visual words for context inference in scene classification
Pattern Recognition
Combining topological and view-based features for 3D model retrieval
Multimedia Tools and Applications
Exploiting geometry in counting grids
SIMBAD'13 Proceedings of the Second international conference on Similarity-Based Pattern Recognition
| Hi-index | 0.01 |
In the statistical learning framework, the use of appropriate kernels may be the key for substantial improvement in solving a given problem. In essence, a kernel is a similarity measure between input points satisfying some mathematical requirements and possibly capturing the domain knowledge. We focus on kernels for images: we represent the image information content with binary strings and discuss various bitwise manipulations obtained using logical operators and convolution with nonbinary stencils. In the theoretical contribution of our work, we show that histogram intersection is a Mercer's kernel and we determine the modifications under which a similarity measure based on the notion of Hausdorff distance is also a Mercer's kernel. In both cases, we determine explicitly the mapping from input to feature space. The presented experimental results support the relevance of our analysis for developing effective trainable systems.