Saliency, Scale and Image Description
International Journal of Computer Vision
Unsupervised Learning of Models for Recognition
ECCV '00 Proceedings of the 6th European Conference on Computer Vision-Part I
A Sparse Object Category Model for Efficient Learning and Exhaustive Recognition
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 1 - Volume 01
A Bayesian Hierarchical Model for Learning Natural Scene Categories
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 2 - Volume 02
Edge-Based Rich Representation for Vehicle Classification
ICCV '05 Proceedings of the Tenth IEEE International Conference on Computer Vision - Volume 2
The Pyramid Match Kernel: Discriminative Classification with Sets of Image Features
ICCV '05 Proceedings of the Tenth IEEE International Conference on Computer Vision - Volume 2
Beyond Bags of Features: Spatial Pyramid Matching for Recognizing Natural Scene Categories
CVPR '06 Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 2
Using Dependent Regions for Object Categorization in a Generative Framework
CVPR '06 Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 2
Pattern Recognition and Machine Learning (Information Science and Statistics)
Pattern Recognition and Machine Learning (Information Science and Statistics)
International Journal of Computer Vision
The Representation and Matching of Pictorial Structures
IEEE Transactions on Computers
A Multimodal Constellation Model for Object Category Recognition
MMM '09 Proceedings of the 15th International Multimedia Modeling Conference on Advances in Multimedia Modeling
ECCV'06 Proceedings of the 9th European conference on Computer Vision - Volume Part IV
Contextual Bag-of-Words for Visual Categorization
IEEE Transactions on Circuits and Systems for Video Technology
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We present an efficient method for object image classification. The method is an extention of the constellation model, which is a part-based model. Generally, constellation model has two weak points. (1) It is essentially a unimodal model which is unsuitable to be applied for categories with many types of appearances. (2) The probability function that represents the constellation model requires a high calculation cost.We introduced multimodalization and speed-up technique to the constellation model to overcome these weak points. The proposed model consists of multiple subordinate constellation models so that diverse types of appearances of an object category could be described by each of them, leading to the increase of description accuracy and consequently, improvement of the classification performance. In this paper, we present how to describe each type of appearance as a subordinate constellationmodel without any prior knowledge regarding the types of appearances, and also the implementation of the extended model's learning in realistic time. In experiments, we confirmed the effectiveness of the proposedmodel by comparison to methods using BoF, and also that the model learning could be realized in realistic time.