ICTACT Journal on Image and Video Processing

K. Seetharaman ¹, S. Muthukumar ²

Affiliations
1 Department of Computer Science and Engineering, Annamalai University, IN
2 Department of Computer Science and Engineering, Manonmaniam Sundaranar University, IN

Abstract

This paper proposes a novel method, based on a statistical probability distributional approach, Hotelling's T2 statistic and Orthogonality test. If the input query image is structured, it is segmented into various regions according to its nature and structure. Otherwise, the image is treated as textured; and it is considered for the experiment as it is. The test statistic T2 is applied on each region and compares it to the target image. If the test of hypothesis is accepted, it is inferred that the query and target images are same or similar. Otherwise, it is assumed that they belong to different groups. Moreover, the Eigen vectors are computed on each region, and the orthogonality test is employed to measure the angle between the two images. The obtained results outperform the existing methods.

Keywords

Query Image, Target Image, Hotelling's T² Statistic, Canberra Distance, Similarity.

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K. Seetharaman ¹, R. Shekhar ²

Affiliations
1 Department of Computer Science and Engineering, Annamalai University, IN
2 Department of Computer Applications, Manonmaniam Sundaranar University, IN

Abstract

This paper proposes a novel technique based on feature fusion using multiple linear regression analysis, and the least-square estimation method is employed to estimate the parameters. The given input query image is segmented into various regions according to the structure of the image. The color and texture features are extracted on each region of the query image, and the features are fused together using the multiple linear regression model. The estimated parameters of the model, which is modeled based on the features, are formed as a vector called a feature vector. The Canberra distance measure is adopted to compare the feature vectors of the query and target images. The F-measure is applied to evaluate the performance of the proposed technique. The obtained results expose that the proposed technique is comparable to the other existing techniques.

Keywords

Regression, Fusion, Feature, F-Measure, Least-Square Estimate.

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K. Seetharaman ¹, T. Hemalatha ²

Affiliations
1 Department of Computer Science & Engineering, Annamalai University, Annamalai Nagar, Tamil Nadu, IN
2 Department of Computer Applications, Jawaharlal Nehru Rajkeeya Mahavidyalaya, Port Blair, IN

Abstract

This paper proposes a simple but efficient scheme for colour image retrieval, based on statistical tests of hypothesis, namely test for equality of variance, test for equality of mean. The test for equality of variance is performed to test the similarity of the query and target images. If the images pass the test, then the test for equality of mean is performed on the same images to examine whether the two images have the same attributes / characteristics. If the query and target images pass the tests then it is inferred that the two images belong to the same class i.e. both the images are same; otherwise, it is assumed that the images belong to different classes i.e. both the images are different. The obtained test statistic values are indexed in ascending order and the image corresponding to the least value is identified as same / similar images. The proposed system is invariant for translation, scaling, and rotation, since the proposed system adjusts itself and treats either the query image or the target image is sample of other. The proposed scheme provides cent percent accuracy if the query and target images are same, whereas there is a slight variation for similar, transformed.

Keywords

Variance, Mean, Query Image, Target Image, Tests of Hypothesis.

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