International Journal of Computer Vision
Generic Object Recognition with Boosting
IEEE Transactions on Pattern Analysis and Machine Intelligence
International Journal of Computer Vision
The Pyramid Match Kernel: Efficient Learning with Sets of Features
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Distinct Multicolored Region Descriptors for Object Recognition
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Kernels on bags for multi-object database retrieval
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Hybrid Generative-Discriminative Visual Categorization
International Journal of Computer Vision
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Robust matching and recognition using context-dependent kernels
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The pyramid match: efficient learning with partial correspondences
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An efficient key point quantization algorithm for large scale image retrieval
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Local normalized linear summation kernel for fast and robust recognition
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A realistic benchmark for visual indoor place recognition
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Classifying materials in the real world
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A hierarchical image kernel with application to pedestrian identification for video surveillance
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Multi-cue discriminative place recognition
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Cue integration through discriminative accumulation
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Local Kernel Feature Analysis (LKFA) for object recognition
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Geometry aware local kernels for object recognition
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Inexact graph matching based on kernels for object retrieval in image databases
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Analysis on a local approach to 3d object recognition
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Properties of patch based approaches for the recognition of visual object classes
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A discriminative approach for the retrieval of images from text queries
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Melanoma recognition using representative and discriminative kernel classifiers
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Halfway through the semantic gap: Prosemantic features for image retrieval
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Object-based image retrieval with kernel on adjacency matrix and local combined features
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Partial correspondence based on subgraph matching
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International Journal of Computer Vision
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Recent developments in computer vision have shown that localfeatures can provide efficient representations suitable for robustobject recognition. Support Vector Machines have been establishedas powerful learning algorithms with good generalizationcapabilities. In this paper, we combine these two approaches andpropose a general kernel method for recognition with localfeatures. We show that the proposed kernel satisfies the Mercercondition and that it is suitable for many established localfeature frameworks. Large-scale recognition results are presentedon three different databases, which demonstrate that SVMs with theproposed kernel perform better than standard matching techniques onlocal features. In addition, experiments on noisy and occludedimages show that local feature representations significantlyoutperform global approaches.