An Analysis on Recent Approaches for Image Captioning

Image captioning is an interdisciplinary area that uses techniques from computer vision and natural language processing to provide a textual description of a picture. The Image captioning task is the process of understanding the scene present in the image by identifying objects and associated actions present to create a meaningful human-like caption which can be used for wide range of applications, including image retrieval, video indexing, assistive technology for the visually impaired, content-based image search, biomedicine, and autonomous cars. Formerly, Machine Learning was utilized for this purpose which will be extensive use of hand-crafted features such as Scale-Invariant Feature Transform (SIFT), Local Binary Patterns (LBP), the Histogram of Oriented Gradients (HOG), and combinations of these features. Extracting handmade characteristics from huge datasets is not straightforward or viable. Many deep learning-based techniques were later proposed. Deep Learning retrieval and template-based approaches were presented; however, both had drawbacks such as losing crucial objects. Recent breakthroughs in deep learning and natural language processing have resulted in considerable increases in image captioning system performance which involves adopting attention mechanisms, transformer-based architectures, multi modal connections, Object-Detection based encoder-decoder and many others. In this survey will explore some of the most recent techniques for image captioning, the datasets and evaluation measures that have been employed in deep learning-based automatic image captioning. The ultimate intention of this study is to act as a guide for researchers by emphasizing future directions for research work. Index Terms: image captioning, computer vision, deep learning, Textual description, natural language processing.