Design, Analysis, and Implementation of Efficient Framework for Image Annotation

Abstract

In this article, a general framework of image annotation is proposed by involving salient object detection (SOD), feature extraction, feature selection, and multi-label classification. For SOD, Augmented-Gradient Vector Flow (A-GVF) is proposed, which fuses benefits of GVF and Minimum Directional Contrast. The article also proposes to control the background information to be included for annotation. This article brings about a comprehensive study of all major feature selection methods for a study on four publicly available datasets. The study concludes with the proposition of using Fisher's method for reducing the dimension of features. Moreover, this article also proposes a set of features that are found to be strong discriminants by most of the methods. This reduced set for image annotation gives 3-4% better accuracy across all the four datasets. This article also proposes an improved multi-label classification algorithm C-MLFE. © 2020 ACM.