Spatial interest pixels (SIPs): Useful low-level features of visual media data

Qi Li, Jieping Ye, Chandra Kambhamettu

Research output: Chapter in Book/Report/Conference proceedingConference contribution

3 Scopus citations

Abstract

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.

Original languageEnglish (US)
Title of host publicationProceedings - 3rd IEEE International Conference on Data Mining, ICDM 2003
Pages163-170
Number of pages8
StatePublished - Dec 1 2003
Event3rd IEEE International Conference on Data Mining, ICDM '03 - Melbourne, FL, United States
Duration: Nov 19 2003Nov 22 2003

Publication series

NameProceedings - IEEE International Conference on Data Mining, ICDM
ISSN (Print)1550-4786

Other

Other3rd IEEE International Conference on Data Mining, ICDM '03
CountryUnited States
CityMelbourne, FL
Period11/19/0311/22/03

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ASJC Scopus subject areas

  • Engineering(all)

Cite this

Li, Q., Ye, J., & Kambhamettu, C. (2003). Spatial interest pixels (SIPs): Useful low-level features of visual media data. In Proceedings - 3rd IEEE International Conference on Data Mining, ICDM 2003 (pp. 163-170). (Proceedings - IEEE International Conference on Data Mining, ICDM).