Intelligent Water Drop Algorithm based Relevant Image Fetching using Histogram and Annotation Features
Saket Jain1, Rajendra Gupta2

1Saket Jain *, Dept. of Computer Science & Engg., Rabindranath Tagore University, Bhopal, India.
2Dr. Rajendra Gupta, Dept. of Computer Science & Engg, Rabindranath Tagore University, Bhopal, India.
Manuscript received on January 29, 2020. | Revised Manuscript received on February 01, 2020. | Manuscript published on March 30, 2020. | PP: 23-29 | Volume-8 Issue-6, March 2020. | Retrieval Number: F6983038620/2020©BEIESP | DOI: 10.35940/ijrte.F6983.038620

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Abstract: Social media network increase trend of image collection at various platforms. Hence getting an relevant image as per query image or text is depend on retrieval algorithms. Number of researcher has proposed algorithms for fetching relevant images, but relevancy of those still need improvement. Hence proposed paper has utilized the Intelligent water Drop algorithm for initial clustering of images as per feature values. Clustering or relevancy of an image depends on visual feature histogram and annotation similarity. Here property of moving a water drop from one node to another in a water drop graph has increase the clustering accuracy of the work. Experiment was done on real dataset having five different group of image set with annotation. Result shows that proposed work has increase the retrieval relevancy accuracy as well as reduce the fetching of the images. This reduction of time was obtained by using the clustering structure of image dataset.
Keywords: Digital Image Processing, Information Extraction, feature extraction, Re-ranking.
Scope of the Article: Digital System and Logic Design