Algorithms for clustering data
Algorithms for clustering data
Extensions to the k-Means Algorithm for Clustering Large Data Sets with Categorical Values
Data Mining and Knowledge Discovery
Modeling the Shape of the Scene: A Holistic Representation of the Spatial Envelope
International Journal of Computer Vision
Video Google: A Text Retrieval Approach to Object Matching in Videos
ICCV '03 Proceedings of the Ninth IEEE International Conference on Computer Vision - Volume 2
Distinctive Image Features from Scale-Invariant Keypoints
International Journal of Computer Vision
Creating Efficient Codebooks for Visual Recognition
ICCV '05 Proceedings of the Tenth IEEE International Conference on Computer Vision (ICCV'05) Volume 1 - Volume 01
Object Categorization by Learned Universal Visual Dictionary
ICCV '05 Proceedings of the Tenth IEEE International Conference on Computer Vision - Volume 2
Scene Classification Using a Hybrid Generative/Discriminative Approach
IEEE Transactions on Pattern Analysis and Machine Intelligence
Universal and Adapted Vocabularies for Generic Visual Categorization
IEEE Transactions on Pattern Analysis and Machine Intelligence
Randomized Clustering Forests for Image Classification
IEEE Transactions on Pattern Analysis and Machine Intelligence
Supervised Learning of Quantizer Codebooks by Information Loss Minimization
IEEE Transactions on Pattern Analysis and Machine Intelligence
Category sensitive codebook construction for object category recognition
ICIP'09 Proceedings of the 16th IEEE international conference on Image processing
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The bag-of-words model has been widely employed in image classification and object detection tasks. The performance of bagof-words methods depends fundamentally on the visual vocabulary that is applied to quantize the image features into visual words. Traditional vocabulary construction methods (e.g. k-means) are unable to capture the semantic relationship between image features. In order to increase the discriminative power of the visual vocabulary, this paper proposes a technique to construct a supervised visual vocabulary by jointly considering image features and their class labels. The method uses a novel cost function in which a simple and effective dissimilarity measure is adopted to deal with category information. And, we adopt a prototypebased approach which tries to find prototypes for clusters instead of using the means in k-means algorithm. The proposed method works as the k-means algorithm by efficiently minimizing a clustering cost function. The experiments on different datasets show that the proposed vocabulary construction method is effective for image classification.