Applying Aggregation Concepts for Image Search

Through the influx of information content on the Internet, a number of image search methodologies have been presented and implemented to increase the accuracy of image retrieval including keywords, object classification and feature processing. Both keyword and object classification models rely heavi...

Full description

Saved in:
Bibliographic Details
Published in2008 Tenth IEEE International Symposium on Multimedia pp. 328 - 333
Main Authors Marshall, B., Wilson, D.-M.
Format Conference Proceeding
LanguageEnglish
Published IEEE 01.12.2008
Subjects
Algorithms
Content based retrieval
Histograms
Humans
Image databases
Image retrieval
Information retrieval
Information technology
Labeling
Search methods
Shape
Testing
Online AccessGet full text
DOI10.1109/ISM.2008.80

Cover

More Information
Summary:Through the influx of information content on the Internet, a number of image search methodologies have been presented and implemented to increase the accuracy of image retrieval including keywords, object classification and feature processing. Both keyword and object classification models rely heavily on human subjects, which is time-consuming and error-prone with inconsistency in word agreement. We propose two feature processing methods without human intervention. The feature collage algorithm compares images based on particular features such as color histogram whereas the feature independent algorithm considers each feature's dimension as independent contributors to the image quality. Using query-by-example, we organize images using rank aggregation methods, previously applied in text information retrieval. We show through empirical experimentation the benefits of our feature processing algorithms over traditional CBIR approaches.
DOI:10.1109/ISM.2008.80