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Spatial Interest Pixels (SIPs): Useful Low-Level Features of Visual Media Data
Melbourne, Florida November 19-November 22
DOI Bookmark: http://doi.ieeecomputersociety.org/10.1109/ICDM.2003.1250916Third IEEE International Conference o ...
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Qi Li, Jieping Ye, Chandra Kambhamettu, "Spatial Interest Pixels (SIPs): Useful Low-Level Features of Visual Media Data," Data Mining, IEEE International Conference on, pp. 163, Third IEEE International Conference on Data Mining (ICDM'03), 2003.
 
 
BibTex
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@article{ 10.1109/ICDM.2003.1250916,
author = {Qi Li and Jieping Ye and Chandra Kambhamettu},
title = {Spatial Interest Pixels (SIPs): Useful Low-Level Features of Visual Media Data},
journal ={Data Mining, IEEE International Conference on},
volume = {0},
year = {2003},
isbn = {0-7695-1978-4},
pages = {163},
doi = {http://doi.ieeecomputersociety.org/10.1109/ICDM.2003.1250916},
publisher = {IEEE Computer Society},
address = {Los Alamitos, CA, USA},
}
 
 
RefWorks Procite/RefMan/Endnote
x 
 
TY - CONF
JO - Data Mining, IEEE International Conference on
TI - Spatial Interest Pixels (SIPs): Useful Low-Level Features of Visual Media Data
SN - 0-7695-1978-4
SP
EP
A1 - Qi Li,
A1 - Jieping Ye,
A1 - Chandra Kambhamettu,
PY - 2003
KW - null
VL - 0
JA - Data Mining, IEEE International Conference on
ER -
 
Qi Li, University of Delaware
Jieping Ye, University of Minnesota
Chandra Kambhamettu, University of Delaware
Visual media data such as an image is the raw data representation for many important applications. The biggest challenge in using visual media data comes from the extremely high dimensionality. We present a comparative study on spatial interest pixels (SIPs), including eight-way (a novel SIP miner), Harris, and Lucas-Kanade, whose extraction is considered as an important step in reducing the dimensionality of visual media data. With extensive case studies, we have shown the usefulness of SIPs as the low-level features of visual media data. A class-preserving dimension reduction algorithm (using GSVD) is applied to further reduce the dimension of feature vectors based on SIPs. The experiments showed its superiority over PCA.
Citation:
Qi Li, Jieping Ye, Chandra Kambhamettu, "Spatial Interest Pixels (SIPs): Useful Low-Level Features of Visual Media Data," icdm, pp.163, Third IEEE International Conference on Data Mining (ICDM'03), 2003
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