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 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 has given reasonable performance on standard datasets In this work, we propose a new model for image annotation known as JSVM which treats annotation as a retrieval problem. In this work, we introduce an JSVM 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 produce a final annotation. The result of the proposed technique shows that this technique outperforms the current state of art methods on the standard datasets.

JEC SVM image annotation image retrieval Radial Basis Function