Abstract
In this paper, we propose a new technique based on wavelet vector quantization for the storage and retrieval of compressed images. Here, the images are first decomposed using wavelet transform followed by vector quantization of the transform coefficients. We note that similar images map to similar labels. Hence, the labels corresponding to an image constitute a feature vector which is used as an index to store and retrieve the image. In addition, the lowest resolution subimages resulting from the wavelet decomposition serve as visual icons for browsing purposes. The proposed technique provide fast access to the compressed images in the database has a lower cost for computing and storing the indices compared to other techniques reported in the literature.
| Original language | English (US) |
|---|---|
| Title of host publication | Proceedings of SPIE - The International Society for Optical Engineering |
| Editors | C.-C.J. Kuo |
| Pages | 269-275 |
| Number of pages | 7 |
| Volume | 2606 |
| State | Published - 1995 |
| Externally published | Yes |
| Event | Digital Image Storage and Archiving Systems - Philadelphia, PA, USA Duration: Oct 25 1995 → Oct 26 1995 |
Other
| Other | Digital Image Storage and Archiving Systems |
|---|---|
| City | Philadelphia, PA, USA |
| Period | 10/25/95 → 10/26/95 |
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ASJC Scopus subject areas
- Electrical and Electronic Engineering
- Condensed Matter Physics
Cite this
Image indexing using wavelet vector quantization. / Idris, Fayez M.; Panchanathan, Sethuraman.
Proceedings of SPIE - The International Society for Optical Engineering. ed. / C.-C.J. Kuo. Vol. 2606 1995. p. 269-275.Research output: Chapter in Book/Report/Conference proceeding › Conference contribution
}
TY - GEN
T1 - Image indexing using wavelet vector quantization
AU - Idris, Fayez M.
AU - Panchanathan, Sethuraman
PY - 1995
Y1 - 1995
N2 - In this paper, we propose a new technique based on wavelet vector quantization for the storage and retrieval of compressed images. Here, the images are first decomposed using wavelet transform followed by vector quantization of the transform coefficients. We note that similar images map to similar labels. Hence, the labels corresponding to an image constitute a feature vector which is used as an index to store and retrieve the image. In addition, the lowest resolution subimages resulting from the wavelet decomposition serve as visual icons for browsing purposes. The proposed technique provide fast access to the compressed images in the database has a lower cost for computing and storing the indices compared to other techniques reported in the literature.
AB - In this paper, we propose a new technique based on wavelet vector quantization for the storage and retrieval of compressed images. Here, the images are first decomposed using wavelet transform followed by vector quantization of the transform coefficients. We note that similar images map to similar labels. Hence, the labels corresponding to an image constitute a feature vector which is used as an index to store and retrieve the image. In addition, the lowest resolution subimages resulting from the wavelet decomposition serve as visual icons for browsing purposes. The proposed technique provide fast access to the compressed images in the database has a lower cost for computing and storing the indices compared to other techniques reported in the literature.
UR - http://www.scopus.com/inward/record.url?scp=0029482916&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=0029482916&partnerID=8YFLogxK
M3 - Conference contribution
AN - SCOPUS:0029482916
SN - 0819419702
SN - 9780819419705
VL - 2606
SP - 269
EP - 275
BT - Proceedings of SPIE - The International Society for Optical Engineering
A2 - Kuo, C.-C.J.
ER -