Object & scene recognition using color descriptors and adaptive color KLT
by Bagci, Volkan Halil, M.S., UNIVERSITY OF ARKANSAS AT LITTLE ROCK, 2011, 49 pages; 1496805

Abstract:

With the emergence and explosion of huge image databases there is an increasing necessity for effective methods to assess visual information on the level of objects and scene types. A wide variety of Content-Based Image Retrieval (CBIR) systems already exists. As a key issue in CBIR, similarity measure quantifies the resemblance in contents between a pair of images.

Depending on the type of features, the formulation of the similarity measure varies greatly. The primary goal of our study is to build a CBIR system based on color features using Adaptive Color Karhunen Loeve Transform (ACKLT) which is efficient and robust.

The results are showing the advantage of the new algorithm for ACKLT. Based on the experimental results, we concluded that correct selection of descriptors invariant to light intensity and light color changes affects object and scene category recognition.
 
AdviserMariofanna Milanova
SchoolUNIVERSITY OF ARKANSAS AT LITTLE ROCK
SourceMAI/ 50-01, p. , Aug 2011
Source TypeThesis
SubjectsComputer science
Publication Number1496805
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