Vector quantization and signal compression
Vector quantization and signal compression
International Journal of Computer Vision
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
Vector Quantization
Object Recognition from Local Scale-Invariant Features
ICCV '99 Proceedings of the International Conference on Computer Vision-Volume 2 - Volume 2
Distinctive Image Features from Scale-Invariant Keypoints
International Journal of Computer Vision
Image retrieval using color histograms generated by Gauss mixture vector quantization
Computer Vision and Image Understanding - Special issue on color for image indexing and retrieval
Image retrieval: Ideas, influences, and trends of the new age
ACM Computing Surveys (CSUR)
Features for image retrieval: an experimental comparison
Information Retrieval
HPAT indexing for fast object/scene recognition based on local appearance
CIVR'03 Proceedings of the 2nd international conference on Image and video retrieval
Content-based image retrieval: an application to tattoo images
ICIP'09 Proceedings of the 16th IEEE international conference on Image processing
Vector Quantization Based Index Cube Model for Image Retrieval
PSIVT '10 Proceedings of the 2010 Fourth Pacific-Rim Symposium on Image and Video Technology
Content-Based image retrieval via vector quantization
ISVC'05 Proceedings of the First international conference on Advances in Visual Computing
Storage and retrieval of compressed images
IEEE Transactions on Consumer Electronics
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An image index model had been proposed that uses the distortion produced by using vector quantisation (VQ) on block vectors of the image to index the image database. In this paper, the novel SIFT distortion (SD) is used as a similarity measure to enable faster retrieval of images from the index model. Each image is represented as a single distortion value. The SD value is compared with various other similarity measures. The results show higher precision for the SD values for the same recall. SD is scale invariant and hence has higher mean average precision for the retrieval of similar images.