International Journal of Computer Vision
Efficient and effective querying by image content
Journal of Intelligent Information Systems - Special issue: advances in visual information management systems
Texture Features for Browsing and Retrieval of Image Data
IEEE Transactions on Pattern Analysis and Machine Intelligence
Comparing images using color coherence vectors
MULTIMEDIA '96 Proceedings of the fourth ACM international conference on Multimedia
VisualSEEk: a fully automated content-based image query system
MULTIMEDIA '96 Proceedings of the fourth ACM international conference on Multimedia
Statistical methods for speech recognition
Statistical methods for speech recognition
Keyblock: an approach for content-based image retrieval
MULTIMEDIA '00 Proceedings of the eighth ACM international conference on Multimedia
Modern Information Retrieval
Speech and Language Processing: An Introduction to Natural Language Processing, Computational Linguistics, and Speech Recognition
Fast Nearest Neighbor Search in Medical Image Databases
VLDB '96 Proceedings of the 22th International Conference on Very Large Data Bases
Efficient Access to and Retrieval from a Shape Image Database
CBAIVL '98 Proceedings of the IEEE Workshop on Content - Based Access of Image and Video Libraries
Image Indexing Using Color Correlograms
CVPR '97 Proceedings of the 1997 Conference on Computer Vision and Pattern Recognition (CVPR '97)
Recognizing Similarity through a Constrained Non-Rigid Transform
ICPR '96 Proceedings of the 1996 International Conference on Pattern Recognition (ICPR '96) Volume I - Volume 7270
Efficient Retrieval and Spatial Querying of 2D Objects
ICMCS '99 Proceedings of the IEEE International Conference on Multimedia Computing and Systems - Volume 2
Synobins: an intermediate level towards annotation and semantic retrieval
EURASIP Journal on Applied Signal Processing
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Keyblock, which is a new framework we proposed for content-based image retrieval, is a generalization of the text-based information retrieval technology in the image domain. In this framework, keyblocks, which are analogous to keywords in text document retrieval, can be constructed by exploiting the vector quantization method which has been used for image compression. Then an image can be represented as a code matrix in which the elements are the indices of the keyblocks in a codebook. Based on this image representation, information retrieval and database analysis techniques developed in the text domain can be generalized to image retrieval. In this paper, we present new models named n-block models which are the generalization of the n-gram models in language modeling to extract comprehensive image features. The effort to capture context in a text document motivated the n-gram models. Similarly, the attempt to capture the content in an image motivates us to consider the correlations of keyblocks within an image. By comparing the performance of our approach with conventional techniques using color feature and wavelet texture feature, the experimental results demonstrate the effectiveness of these n-block models.