Image Information Retrieval using Wavelet and Curvelet Transform
Muzhir Shaban Al-Ani1, Abdulrahman Dira Khalaf2
1Muzhir Shaban Al-Ani, Assoc. Professor, Computter Science Department, Amman Arab University, Amman, Jordan.
2Abdulrahman Dira Khalaf, Department of Computer Science, University of Anbar, Iraq.
Manuscript received on February 06, 2013. | Revised Manuscript received on February 28, 2013. | Manuscript published on March 05, 2013. | PP: 86-90 | Volume-3 Issue-1, March 2013. | Retrieval Number: A1311033113/2013©BEIESP
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© The Authors. Published By: Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: The rapid growth of multimedia data applications via Internet becomes a big challenge over the world. This research is concentrated on the implementation of an accurate and fast algorithm that retrieves image information based on vector space model. The big challenge of information retrieval is a semantic gap, which is the difference between the human perception of a concept and how it can be represented using a machine- level language. This paper aims to design an information retrieval system based on hybrid algorithm through two stages; first one is training and the second one is testing. This algorithm based on extracted features using Wavelet and Curvelet decomposition and the statistic parameters such as mean, standard deviation and energy of signals. The system is tested over 1000 images which are divided into 10 categories, each category has 100 images. The tested results of system are compared between system based on Wavelet and system based on Histogram. Performance measures are implemented applying two metrics called precision and recall. The results of training phase show that the elapsed time of system based on hybrid Algorithm is greater than the elapsed time based on DWT or Histogram. The Average Retrieving Time (ART) for system based on hybrid algorithm is less than ART based on Wavelet and Histogram.
Keywords: Information Retrieval, Multimedia Information Retrieval, Discrete Wavelet Transform, Curvelet Transform, Vector Space Model, Feature Extraction.