An innovative hybrid hierarchical model for automatic image annotation

Abstract

This paper presents a novel image annotation framework for domains with large numbers of images. Automatic image annotation is such a domain, by which a computer system automatically assigns metadata in the form of captioning or keywords to a digital image. This application of computer vision method is used in image retrieval system to organize and locate images of interest from a database. Many techniques have been proposed for image annotation in the last decade that has given realistic performance on standard datasets. In this work, we introduce an innovative hybrid model for image annotation that treats annotation as a retrieval problem. The proposed technique utilizes low level image features and a simple combination of basic distances using JEC to find the nearest neighbors of a given image; the keywords are then assigned using SVM approach which aims to explore the combination of three different methods. First, the initial annotation of the data using flat wise and axis wise methods, and that takes the hierarchy into consideration by classifying consecutively its instances through position wise method. Finally, we make use of pair wise majority voting between methods by simply summing strings in order to make a final annotation. The result of the proposed technique shows that this technique outperforms the current state of art methods on the normal datasets. © 2012 Springer-Verlag.