Robust voting algorithm based on labels of behavior for video copy detection
MULTIMEDIA '06 Proceedings of the 14th annual ACM international conference on Multimedia
Invariant salient regions based image retrieval under viewpoint and illumination variations
Journal of Visual Communication and Image Representation
Foundations and Trends® in Computer Graphics and Vision
Context-Based Object-Class Recognition and Retrieval by Generalized Correlograms
IEEE Transactions on Pattern Analysis and Machine Intelligence
Image retrieval: Ideas, influences, and trends of the new age
ACM Computing Surveys (CSUR)
Scalable landmark recognition using EXTENT
Multimedia Tools and Applications
Object recognition and segmentation in videos by connecting heterogeneous visual features
Computer Vision and Image Understanding
Robust matching and recognition using context-dependent kernels
Proceedings of the 25th international conference on Machine learning
Learning an Alphabet of Shape and Appearance for Multi-Class Object Detection
International Journal of Computer Vision
Grayscale medical image annotation using local relational features
Pattern Recognition Letters
A boundary-fragment-model for object detection
ECCV'06 Proceedings of the 9th European conference on Computer Vision - Volume Part II
Object recognition using discriminative parts
Computer Vision and Image Understanding
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We present a novel approach for fast object class recognition incorporating contextual information into boosting. The object is represented as a constellation of generalized correlograms that integrate both information of local parts and their spatial relations. Incorporating the spatial relations into our constellation of descriptors, we show that an exhaustive search for the best matching can be avoided. Combining the contextual descriptors with boosting, the system simultaneously learns the information that characterize each part of the object along with their characteristic mutual spatial relations. The proposed framework includes a matching step between homologous parts in the training set, and learning the spatial pattern after matching. In the matching part two approaches are provided: a supervised algorithm and an unsupervised one. Our results are favorably compared against state-of-the-art results.