Vector quantization and signal compression
Vector quantization and signal compression
International Journal of Computer Vision
Video and image processing in multimedia systems
Video and image processing in multimedia systems
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
Image and video indexing using vector quantization
Machine Vision and Applications
SIMPLIcity: Semantics-Sensitive Integrated Matching for Picture LIbraries
IEEE Transactions on Pattern Analysis and Machine Intelligence
Minimum Distortion Color Image Retrieval Based on Lloyd-Clustered Gauss Mixtures
DCC '05 Proceedings of the Data Compression Conference
Storage and retrieval of compressed images
IEEE Transactions on Consumer Electronics
Index model for image retrieval using SIFT distortion
International Journal of Intelligent Information and Database Systems
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Image retrieval and image compression are each areas that have received considerable attention in the past. However there have been fewer advances that address both these problems simultaneously. In this work, we present a novel approach for content-based image retrieval (CBIR) using vector quantization (VQ). Using VQ allows us to retain the image database in compressed form without any need to store additional features for image retrieval. The VQ codebooks serve as generative image models and are used to represent images while computing their similarity. The hope is that encoding an image with a codebook of a similar image will yield a better representation than when a codebook of a dissimilar image is used. Experiments performed on a color image database over a range of codebook sizes support this hypothesis and retrieval based on this method compares well with previous work.