Title :
Semi Supervised Feature Extraction for Filling Semantic Gap in Image Retrieval
Author :
Jalali, Mahdi ; Sedghi, Tohid
Author_Institution :
Naghadeh Branch, Islamic Azad Univ., Naghadeh, Iran
Abstract :
In this paper, a novel framework for combining the texture, shape information, beside that newly introduced transform for textural features are presented. This method is based on Spectral Function that provides a statistical description in the frequency domain of signals, and then the Spectal function (SF) of each signal is calculated by spectral analyzer (SSA). Features are energy and standard deviation of SF of signals got at different regions of bifrequency plane. This scheme shows high performance in Image sets. The experimental results are compared with previous works and are found to be encouraging.
Keywords :
feature extraction; image retrieval; image texture; statistical analysis; SF; SSA; bifrequency plane; image retrieval; semantic gap filling; semisupervised feature extraction; shape information; signal frequency domain; spectral analyzer; spectral function; statistical description; textural features; texture information; Feature extraction; Force; Image edge detection; Image retrieval; Shape; Tiles; Vectors;
Conference_Titel :
Machine Vision and Image Processing (MVIP), 2011 7th Iranian
Conference_Location :
Tehran
Print_ISBN :
978-1-4577-1533-4
DOI :
10.1109/IranianMVIP.2011.6121537
