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
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
ECCV'06 Proceedings of the 9th European conference on Computer Vision - Volume Part IV
A multimodal constellation model for object image classification
Journal on Image and Video Processing - Special issue on selected papers from multimedia modeling conference 2009
PMA: Pixel-based multi-anchor algorithm for image recognition on multi-core systems
Proceedings of the 2012 International Workshop on Programming Models and Applications for Multicores and Manycores
A segmentation-free method for image classification based on pixel-wise matching
Journal of Computer and System Sciences
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Object category recognition in various appearances is one of the most challenging task in the object recognition research fields. The major approach to solve the task is using the Bag of Features (BoF). The constellation model is another approach that has the following advantages: (a) Adding and changing the candidate categories is easy; (b) Its description accuracy is higher than BoF; (c) Position and scale information, which are ignored by BoF, can be used effectively. On the other hand, this model has two weak points: (1) It is essentially an unimodal model that is unsuitable for categories with many types of appearances. (2) The probability function that represents the constellation model takes a long time to calculate. In this paper we propose a "Multimodal Constellation Model" to solve the two weak points of the constellation model. Experimental results showed the effectivity of the proposed model by comparison to methods using BoF.