TY - GEN
T1 - Spatial interest pixels (SIPs)
T2 - 3rd IEEE International Conference on Data Mining, ICDM '03
AU - Li, Qi
AU - Ye, Jieping
AU - Kambhamettu, Chandra
PY - 2003/12/1
Y1 - 2003/12/1
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=26844550244&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=26844550244&partnerID=8YFLogxK
M3 - Conference contribution
AN - SCOPUS:26844550244
SN - 0769519784
SN - 9780769519784
T3 - Proceedings - IEEE International Conference on Data Mining, ICDM
SP - 163
EP - 170
BT - Proceedings - 3rd IEEE International Conference on Data Mining, ICDM 2003
Y2 - 19 November 2003 through 22 November 2003
ER -