New Non-Parametric Model for Automatic Annotations of Images in Annotation Based Image Retrieval

Kanakam Siva Ram Prasad¹

1. Dept. of IT, Sasi Institute of Technology and Engineering, Tadepalligudem, India.

Correspondence should be addressed to: ksrprasad@sasi.ac.in.

Section:Research Paper, Product Type: Isroset-Journal
Vol.5 , Issue.4 , pp.16-21, Aug-2017

Online published on Aug 30, 2017

Copyright © Kanakam Siva Ram Prasad . This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
 

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Abstract :
In this paper we propose an automatic approach to annotating and retrieving images based on a training set of images. We assume that regions in an image can be described using a small vocabulary of blobs. Blobs are generated from image features using clustering. Given a training set of images with annotations, the annotation process implemented in our system is based on CMRM. Using a set of annotated images the system learns the joint distribution of the blobs and concepts in this paper show those probabilistic models which allow predicting the probability of generating a word given the blobs in an image. This may be used to automatically annotate and retrieve images given a word as a query. We show that relevance models. Allow us to derive these probabilities in a natural way. Experiments show that the annotation performance of this cross-media relevance model is almost six times as than a model based on word-blob co-occurrence model and twice as good as a state of the art model derived from machine translation.

Key-Words / Index Term :
Automatic Image Annotation, Content Based Image Retrieval, Semantic Gap, Annotation Based Image Retrieval, relevance model

References :
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