A combined hierarchical model for automatic image annotation and retrieval

Abstract

Automatic image annotation is the process by which a computer system automatically assigns metadata in the form of captioning or keywords to a digital image. This application of computer vision technique 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 gives reasonable 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 two known methods, and that takes the hierarchy into consideration by classifying consecutively its instances; finally, we make use of pair wise majority voting between methods by simply summing strings in order to produce a final annotation. The proposed technique results show that this method outperforms the current state of art methods on the standard datasets.