Survey and Analysis on Machine Learning Approaches for Image Annotation

Abstract

In current years, a large amount of image data is being collected worldwide, which is majorly generated by corporate organizations, health industry and social networking sites. With the strength of substantial level depiction of images, Annotating image has numerous applications not only in image understanding and analysis but also in some of the concern domain like medical research, rural and urban management. Automatic Image Annotation (AIA) has been raised since the late 1990s due to inherent weaknesses of manual image annotation. In this paper, a deep review of the most recent stage in the development of AIA methods is presented by synthesizing 32 literatures published during the past decades. We classify AIA methods into five categories: 1) Kernel Logistic Regression (KLR), 2) Tri-relational Graph (TG), 3) Semantically Regularised CNN- RNN (S-CNN-RNN), 4) Label Correlation guided Deep Multi-view (LCDM), and 5) Multi-Modal Semantic Hash Learning (MMSHL). Considering inspiration on the basis of main idea, framework of model, complexity of computation, time complexity and accuracy in annotation Comparative analysis for various AIA methods are done.