International Journal of Computer Vision
The Pyramid Match Kernel: Efficient Learning with Sets of Features
The Journal of Machine Learning Research
Kernels on bags for multi-object database retrieval
Proceedings of the 6th ACM international conference on Image and video retrieval
Robust matching and recognition using context-dependent kernels
Proceedings of the 25th international conference on Machine learning
Efficiently matching sets of features with random histograms
MM '08 Proceedings of the 16th ACM international conference on Multimedia
A Bag of Strings Representation for Image Categorization
Journal of Mathematical Imaging and Vision
Dimension amnesic pyramid match kernel
AAAI'08 Proceedings of the 23rd national conference on Artificial intelligence - Volume 2
An efficient key point quantization algorithm for large scale image retrieval
LS-MMRM '09 Proceedings of the First ACM workshop on Large-scale multimedia retrieval and mining
Semi---supervised Learning with Constraints for Multi---view Object Recognition
ICANN '09 Proceedings of the 19th International Conference on Artificial Neural Networks: Part II
A novel color-context descriptor and its applications
ICME'09 Proceedings of the 2009 IEEE international conference on Multimedia and Expo
Multitask semi-supervised learning with constraints and constraint exceptions
ICANN'10 Proceedings of the 20th international conference on Artificial neural networks: Part III
Kernel sparse representation for image classification and face recognition
ECCV'10 Proceedings of the 11th European conference on Computer vision: Part IV
An improved pyramid matching kernel
LSMS/ICSEE'10 Proceedings of the 2010 international conference on Life system modeling and and intelligent computing, and 2010 international conference on Intelligent computing for sustainable energy and environment: Part I
Artwork 3D model database indexing and classification
Pattern Recognition
Geometry aware local kernels for object recognition
ACCV'10 Proceedings of the 10th Asian conference on Computer vision - Volume Part I
K-median clustering, model-based compressive sensing, and sparse recovery for earth mover distance
Proceedings of the forty-third annual ACM symposium on Theory of computing
Inexact graph matching based on kernels for object retrieval in image databases
Image and Vision Computing
Dynamic similarity kernel for visual recognition
KES'06 Proceedings of the 10th international conference on Knowledge-Based Intelligent Information and Engineering Systems - Volume Part II
Accurate Object Recognition with Shape Masks
International Journal of Computer Vision
Proceedings of the 2nd ACM International Conference on Multimedia Retrieval
Object-based image retrieval with kernel on adjacency matrix and local combined features
ACM Transactions on Multimedia Computing, Communications, and Applications (TOMCCAP)
Contextual object detection using set-based classification
ECCV'12 Proceedings of the 12th European conference on Computer Vision - Volume Part VI
Efficient image signatures and similarities using tensor products of local descriptors
Computer Vision and Image Understanding
The pooled NBNN kernel: beyond image-to-class and image-to-image
ACCV'12 Proceedings of the 11th Asian conference on Computer Vision - Volume Part I
Proceedings of the 2013 International Symposium on Wearable Computers
Explicit context-aware kernel map learning for image annotation
ICVS'13 Proceedings of the 9th international conference on Computer Vision Systems
Image Classification with the Fisher Vector: Theory and Practice
International Journal of Computer Vision
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A new class of kernels for object recognition based on local image feature representations are introduced in this paper. These kernels satisfy the Mercer condition and incorporate multiple types of local features and semilocal constraints between them. Experimental results of SVM classifiers coupled with the proposed kernels are reported on recognition tasks with the COIL-100 database and compared with existing methods. The proposed kernels achieved competitive performance and were robust to changes in object configurations and image degradations.