Research of Global Features of 3D Color Histogram with Similarity and Dissimilarity Metrics for Image Processing
A. Jeya Christy1, K. Dhanalakshmi2

1A. Jeya Christy, Research Scholar, Department of Computer Science and Engineering, Vaigai College of Engineering, Madurai (Tamil Nadu), India.

2Dr. K. Dhanalakshmi, Professor, Department of Computer Science and Engineering, PSNA College of Engineering and Technology, Dindigul (Tamil Nadu), India.

Manuscript received on 11 January 2020 | Revised Manuscript received on 07 February 2020 | Manuscript Published on 20 February 2020 | PP: 359-365 | Volume-9 Issue-3S January 2020 | Retrieval Number: C10780193S20/2020©BEIESP | DOI: 10.35940/ijitee.C1078.0193S20

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Abstract: A review of the global feature color comprising of seven different metrics are discussed here. The color is the most powerful feature for describing the images. This study contains four sections. The first section explains the related techniques used in various papers. The second section explains the two different kinds of metrics. (1) Similarity metrics such as Cosine and Correlation and (2) Dissimilarity metrics such as Euclidean, Manhattan, Bhattacharyya, Chi-Squared and Intersection. The third section explains experiment results using CALTECHUCSD Birds-200 image library. The fourth section gives the conclusion and future work. In this experiment, the query image can be divided into trained (indexed) or untrained (non-indexed). In the similarity metric analysis, the experimental results show that the cosine similarity gives better similarity score than correlation. Similarly, in the dissimilarity metric analysis, the Bhattacharyya gives a better result than other distance metrics.

Keywords: Color Histogram, Similarity metric, Dissimilarity Metric, Cosine Similarity, Correlation Euclidean, Manhattan, Bhattacharyya, Chi-Squared and Intersection.
Scope of the Article: Image analysis and Processing