Blobworld: Image Segmentation Using Expectation-Maximization and Its Application to Image Querying
IEEE Transactions on Pattern Analysis and Machine Intelligence
Support vector machine active learning with applications to text classification
The Journal of Machine Learning Research
Recognition with Local Features: the Kernel Recipe
ICCV '03 Proceedings of the Ninth IEEE International Conference on Computer Vision - Volume 2
Kernel Methods for Pattern Analysis
Kernel Methods for Pattern Analysis
Distinctive Image Features from Scale-Invariant Keypoints
International Journal of Computer Vision
Image Categorization by Learning and Reasoning with Regions
The Journal of Machine Learning Research
Mercer Kernels for Object Recognition with Local Features
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 2 - Volume 02
Efficient Image Matching with Distributions of Local Invariant Features
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 2 - Volume 02
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
Extraction of Windows in Facade Using Kernel on Graph of Contours
SCIA '09 Proceedings of the 16th Scandinavian Conference on Image Analysis
Spatio-temporal tube kernel for actor retrieval
ICIP'09 Proceedings of the 16th IEEE international conference on Image processing
Object-based image retrieval with kernel on adjacency matrix and local combined features
ACM Transactions on Multimedia Computing, Communications, and Applications (TOMCCAP)
Efficient image signatures and similarities using tensor products of local descriptors
Computer Vision and Image Understanding
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In this paper, a kernel-based method for multi-object retrieval in large image database is presented. First, our approach exploits a fuzzy region segmentation approach in order to get robust local feature extraction and characterization. All the region features are summarized in bags representing the image index. The main part of this work concerns the kernel functions to deal with sets of features. Based on the linear combination of minor kernels, a family of kernels on bags is introduced. Several weighting schemes and combinations are proposed. Their introduction are motivated in the specific context of dealing with multiobject recognition with heterogeneous background. Combined with SVMs classification and interactive online learning framework, the resulting algorithm satisfies the robustness requirements for representation and classification of objects. Experiments and comparisons demonstrate the good performances of our multi-object retrieval technique.