Publication details

Illumination Invariants Based on Markov Random Fields

Monography Chapter

Vácha Pavel, Haindl Michal


serial: Pattern Recognition, Recent Advances, p. 253-272 , Eds: Herout A.

research: CEZ:AV0Z10750506

project(s): 1M0572, GA MŠk, 2C06019, GA MŠk, GA102/08/0593, GA ČR

keywords: illumination invariants, textural features, Markov random fields

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abstract (eng):

Content-based image retrieval systems (CBIR) typically query large image databases based on some automatically generated colour and textural features. Optimal robust features should be geometry and illumination invariant. Although image retrieval has been an active research area for many years this difficult problem is still far from being solved. We introduce fast and robust textural features that allow retrieving images with similar scenes comprising colour textured objects viewed with different illumination. The proposed textural features that are invariant to illumination spectrum and extremely robust to illumination direction. They require only a single training image per texture and no knowledge of illumination direction, brightness or spectrum. These feature utilises utilise illumination invariant features extracted from three different Markov random field (MRF) based texture representations.

RIV: BD