Invariant and Perceptually Consistent Texture Mapping for Content-Based Image Retrieval

Huizhong Long, Chee-Wee Tan, Wee Kheng Leow

Publikation: Bidrag til bog/antologi/rapportKonferencebidrag i proceedingsForskningpeer review

Abstrakt

Texture is an important visual feature for content-based image retrieval. An ideal content-based retrieval system should compare images in its database with the query in a manner that is consistent with human's perception of visual similarity. Moreover, texture matching should be invariant to texture scale and orientation because the same texture can appear in the images in varying scales and orientations. In practice, however, texture similarity computed using computational texture features is not necessarily consistent with human's perception. This paper presents a method of mapping texture features into a texture space that is scale and orientation invariant, and at the same time, consistent with human's perception. Test results show that this method achieves a better retrieval performance than methods that are not invariant and not perceptually consistent
OriginalsprogEngelsk
TitelImage Processing, 2001 : Proceedings. 2001 International Conference on
Vol/bind2
Udgivelses stedNew York
ForlagIEEE
Publikationsdato2001
Sider117-120
ISBN (Trykt)0780367251
DOI
StatusUdgivet - 2001
Udgivet eksterntJa
BegivenhedInternational Conference on Image Processing 2001 - Thessaloniki, Grækenland
Varighed: 7 okt. 200110 okt. 2001
Konferencens nummer: 1
http://conferences.visionbib.com/2001/icip-10-01-call.pdf

Konference

KonferenceInternational Conference on Image Processing 2001
Nummer1
LandGrækenland
ByThessaloniki
Periode07/10/200110/10/2001
Internetadresse

Emneord

  • Content-based retrieval
  • Image matching
  • Image texture
  • Neural nets
  • Visual databases
  • Visual perception

Citationsformater

Long, H., Tan, C-W., & Leow, W. K. (2001). Invariant and Perceptually Consistent Texture Mapping for Content-Based Image Retrieval. I Image Processing, 2001: Proceedings. 2001 International Conference on (Bind 2, s. 117-120). IEEE. https://doi.org/10.1109/ICIP.2001.958438