A Review of Methods of Instance-based Automatic Image Annotation

Abstract

Abstract-Today, to use automatic image annotation in order to fill the semantic gap between low level features of images and understanding their information in retriev ing process has become popular. Since automatic image annotation is crucial in understanding digital images several methods have been proposed to automatically annotate an image. One of the most important of these methods is instance-based image annotation. As these methods are vastly used in this paper, the most impo rtant instance-based image annotation methods are analy zed. First of all the main parts of instance-based automatic image annotation are analyzed. Afterwards, the main methods of instance-based automatic image annotation are reviewed and co mpared based on various features. In the end the most important challenges and open-ended fields in instance-based image annotation are analyzed.

A Novel Evolutionary Automatic Data Clustering Algorithm using Teaching-Learning-Based Optimization

“…Conventional clustering, needs input parameters to group and render best partitions in a given dataset, and supplemented with reliable guidelines to evaluate the clusters. The procedure of evaluating the results of a clustering algorithm is known under the term cluster validity [26]. CVIs are classified in to three categories: internal, external and relative, these CVIs are defined by combination of separability and compactness.…”