Normalized Cuts and Image Segmentation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Content-Based Image Retrieval at the End of the Early Years
IEEE Transactions on Pattern Analysis and Machine Intelligence
Introduction to Modern Information Retrieval
Introduction to Modern Information Retrieval
Laplacian Eigenmaps for dimensionality reduction and data representation
Neural Computation
Segmentation Using Eigenvectors: A Unifying View
ICCV '99 Proceedings of the International Conference on Computer Vision-Volume 2 - Volume 2
Introduction to MPEG-7: Multimedia Content Description Interface
Introduction to MPEG-7: Multimedia Content Description Interface
Recognizing faces with PCA and ICA
Computer Vision and Image Understanding - Special issue on Face recognition
ViVo: Visual Vocabulary Construction for Mining Biomedical Images
ICDM '05 Proceedings of the Fifth IEEE International Conference on Data Mining
IEEE Transactions on Pattern Analysis and Machine Intelligence
Image Retrieval Using Multimodal Keywords
ISM '06 Proceedings of the Eighth IEEE International Symposium on Multimedia
Journal of Cognitive Neuroscience
Multiobjective data clustering
CVPR'04 Proceedings of the 2004 IEEE computer society conference on Computer vision and pattern recognition
Image clustering using multimodal keywords
SAMT'06 Proceedings of the First international conference on Semantic and Digital Media Technologies
Narrowing the semantic gap - improved text-based web document retrieval using visual features
IEEE Transactions on Multimedia
Image classification for content-based indexing
IEEE Transactions on Image Processing
Cartoon features selection using Diffusion Score
Signal Processing
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In the clustering of large number of images using low-level features, one of the problems encountered is the high dimensional feature space. The high dimensionality of feature spaces leads to unnecessary cost in feature selection and also in the distance measurement during the clustering process. In this paper, we propose an approach to reduce the dimensionality of the feature space based on diffusion maps. In the proposed approach, each image is represented by a set of tiles. A visual keyword-image matrix is derived from classifying these tiles into a set of clusters and counting the occurrence of each cluster in each image of our database. The visual keyword-image matrix is similar to the term-document matrix in information retrieval. We use diffusion maps to reduce the dimensionality of visual keyword matrix. By reducing the dimensionality of the image representation, we can save computation cost significantly. We compare the performance between the proposed approach and the approach that uses the global MPEG-7 color descriptors. The results demonstrate the improvements.