Open AccessColor Image Retrieval Based on Non-Parametric Statistical Tests of Hypothesis
Author(s) -
Ravi Shekhar,
K. Seetharaman
Publication year - 2014
Publication title -
computer science and information technology ( cs and it )
Language(s) - English
Resource type - Conference proceedings
DOI - 10.5121/csit.2014.4912
Subject(s) - computer science , artificial intelligence , statistical hypothesis testing , parametric statistics , image retrieval , pattern recognition (psychology) , statistical analysis , image (mathematics) , information retrieval , computer vision , mathematics , statistics
A novel method for color image retrieval, based on statistical non-parametric tests such as twosample\udWald Test for equality of variance and Man-Whitney U test, is proposed in this paper.\udThe proposed method tests the deviation, i.e. distance in terms of variance between the query\udand target images; if the images pass the test, then it is proceeded to test the spectrum of\udenergy, i.e. distance between the mean values of the two images; otherwise, the test is dropped.\udIf the query and target images pass the tests then it is inferred that the two images belong to the\udsame class, i.e. both the images are same; otherwise, it is assumed that the images belong to\uddifferent classes, i.e. both images are different. The proposed method is robust for scaling and\udrotation, since it adjusts itself and treats either the query image or the target image is the\udsample of other. \u