Spatial Interest Pixels (SIPs): Useful Low-Level Features of Visual Media Data

Authors:
Qi Li;Jieping Ye;Chandra Kambhamettu
Affiliations:
-;-;-
Venue:
ICDM '03 Proceedings of the Third IEEE International Conference on Data Mining
Year:
2003

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Abstract

Visual media data such as an image is the raw data representationfor many important applications. The biggestchallenge in using visual media data comes from the extremelyhigh dimensionality. We present a comparativestudy on spatial interest pixels (SIPs), including eight-way(a novel SIP miner), Harris, and Lucas-Kanade, whose extractionis considered as an important step in reducing thedimensionality of visual media data. With extensive casestudies, we have shown the usefulness of SIPs as the low-levelfeatures of visual media data. A class-preserving dimensionreduction algorithm (using GSVD) is applied tofurther reduce the dimension of feature vectors based onSIPs. The experiments showed its superiority over PCA.