Fundamentals of digital image processing
Fundamentals of digital image processing
Multimedia: computing, communications and applications
Multimedia: computing, communications and applications
Digital image processing
Video parsing and browsing using compressed data
Multimedia Tools and Applications
The Illumination-Invariant Matching of Deterministic Local Structure in Color Images
IEEE Transactions on Pattern Analysis and Machine Intelligence
Content-based retrieval using heuristic search
Proceedings of the 22nd annual international ACM SIGIR conference on Research and development in information retrieval
Content-Based Image Retrieval at the End of the Early Years
IEEE Transactions on Pattern Analysis and Machine Intelligence
The Earth Mover's Distance as a Metric for Image Retrieval
International Journal of Computer Vision
Performance evaluation in content-based image retrieval: overview and proposals
Pattern Recognition Letters - Special issue on image/video indexing and retrieval
A web-based evaluation system for CBIR
MULTIMEDIA '01 Proceedings of the 2001 ACM workshops on Multimedia: multimedia information retrieval
Scalable Color Image Indexing and Retrieval Using Vector Wavelets
IEEE Transactions on Knowledge and Data Engineering
Virtual Images for Similarity Retrieval in Image Databases
IEEE Transactions on Knowledge and Data Engineering
ImageMap: An Image Indexing Method Based on Spatial Similarity
IEEE Transactions on Knowledge and Data Engineering
Wavelet-Based Salient Points: Applications to Image Retrieval Using Color and Texture Features
VISUAL '00 Proceedings of the 4th International Conference on Advances in Visual Information Systems
Multimedia Systems - Special section on video libraries
Image Indexing Using Color Correlograms
CVPR '97 Proceedings of the 1997 Conference on Computer Vision and Pattern Recognition (CVPR '97)
Efficient Indexing of Multi-Color Sets for Content-Based Image Retrieval
SSIAI '00 Proceedings of the 4th IEEE Southwest Symposium on Image Analysis and Interpretation
Image Indexing Using Weighted Color Histogram
ICIAP '99 Proceedings of the 10th International Conference on Image Analysis and Processing
An Efficient Color-Spatial Indexing and Retrieval Scheme for Image Database
ICPADS '00 Proceedings of the Seventh International Conference on Parallel and Distributed Systems: Workshops
Automatic Image Indexing for Rapid Content-Based Retrieval
IW-MMDBMS '96 Proceedings of the 1996 International Workshop on Multi-Media Database Management Systems (IW-MMDBMS '96)
Integrated Image and Speech Analysis for Content-Based Video Indexing
ICMCS '96 Proceedings of the 1996 International Conference on Multimedia Computing and Systems
IEEE Transactions on Multimedia
Image indexing using moments and wavelets
IEEE Transactions on Consumer Electronics
Image categorization: Graph edit distance+edge direction histogram
Pattern Recognition
A flexible framework to ease nearest neighbor search in multidimensional data spaces
Data & Knowledge Engineering
International Journal of Intelligent Systems Technologies and Applications
A hierarchical semantic-based distance for nominal histogram comparison
Data & Knowledge Engineering
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For efficient image retrieval, the image database should be processed to extract a representing feature vector for each member image in the database. A reliable and robust statistical image indexing technique based on a stochastic model of an image color content has been developed. Based on the developed stochastic model, a compact 12-dimensional feature vector was defined to tag images in the database system. The entries of the defined feature vector are the mean, variance, and skewness of the image color histogram distributions as well as correlation factors between color components of the RGB color space. It was shown using statistical analysis that the feature vector provides sufficient knowledge about the histogram distribution. The reliability and robustness of the proposed technique against common intensity artifacts and noise was validated through several experiments conducted for that purpose. The proposed technique outperforms traditional and other histogram based techniques in terms of feature vector size and properties, as well as performance.