The work in this thesis proposes an image understanding algorithm for automatically identifying and ranking different image regions into several levels of importance. Given a color image, specialized maps for classifying image content namely: weighted similarity, weighted homogeneity, image contrast and memory color maps are generated and combined to provide a perceptual importance map. Further analysis of this map yields a region ranking map which sorts the image content into different levels of significance.

The algorithm was tested on a large database that contains a variety of color images. Those images were acquired from the Berkeley segmentation dataset as well as internal images. Experimental results show that our technique matches human manual ranking with 90% efficiency.

Applications of the proposed algorithm include image rendering, classification, indexing and retrieval. Adaptive compression and camera auto-focus are other potential applications.

Library of Congress Subject Headings

Image analysis--Data processing; Image processing--Digital techniques

Publication Date


Document Type


Student Type


Degree Name

Electrical Engineering (MS)

Department, Program, or Center

Electrical Engineering (KGCOE)


Eli Saber

Advisor/Committee Member

Sohail A. Dianat

Advisor/Committee Member

Vincent Amuso


Physical copy available from RIT's Wallace Library at TA1637 .J32 2007


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