Open AccessIntelligent Water Drop Algorithm based Relevant Image Fetching using Histogram and Annotation Features
Author(s) -
Saket Jain,
Rajendra Gupta
Publication year - 2020
Publication title -
international journal of recent technology and engineering
Language(s) - English
Resource type - Journals
ISSN - 2277-3878
DOI - 10.35940/ijrte.f6983.038620
Subject(s) - cluster analysis , computer science , histogram , image retrieval , artificial intelligence , automatic image annotation , annotation , data mining , pattern recognition (psychology) , graph , image (mathematics) , theoretical computer science
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.