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A Hybrid Approach to Content-Based Image Retrieval
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Author(s): Görkem Asilioglu (TOBB ETÜ, Turkey), Emine Merve Kaya (TOBB ETÜ, Turkey), Duygu Sarikaya (TOBB ETÜ, Turkey), Shang Gao (University of Calgary, Canada), Tansel Ozyer (TOBB ETÜ, Turkey), Jamal Jida (Lebanese University, Lebanon)and Reda Alhajj (University of Calgary, Canada & Global University, Lebanon)
Copyright: 2012
Pages: 14
Source title:
Intelligent Multimedia Databases and Information Retrieval: Advancing Applications and Technologies
Source Author(s)/Editor(s): Li Yan (Northeastern University, China)and Zongmin Ma (Northeastern University, China)
DOI: 10.4018/978-1-61350-126-9.ch006
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Abstract
Digital image storage and retrieval is gaining more popularity due to the rapidly advancing technology and the large number of vital applications, in addition to flexibility in managing personal collections of images. Traditional approaches employ keyword based indexing which is not very effective. Content based methods are more attractive though challenging and require considerable effort for automated feature extraction. In this chapter, we present a hybrid method for extracting features from images using a combination of already established methods, allowing them to be compared to a given input image as seen in other query-by-example methods. First, the image features are calculated using Edge Orientation Autocorrelograms and Color Correlograms. Then, distances of the images to the original image will be calculated using the L1 distance feature separately for both features. The distance sets will then be merged according to a weight supplied by the user. The reported test results demonstrate the applicability and effectiveness of the proposed approach.
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