Features to Text: A Comprehensive Survey of Deep Learning on Semantic Segmentation and Image Captioning. (23rd March 2021)

• Record Type: Journal Article
• Authors: Oluwasammi, Ariyo; Aftab, Muhammad Umar; Qin, Zhiguang; Ngo, Son Tung; Doan, Thang Van; Nguyen, Son Ba; Nguyen, Son Hoang; Nguyen, Giang Hoang
• Other Names: Selisteanu Dan Academic Editor.
• Is Part Of: Complexity. Volume 2021(2021)
• Publication Date: 2021-03-23
• Subjects: Chaotic behavior in systems -- Periodicals; Complexity (Philosophy) -- Periodicals; 003
• DOI: 10.1155/2021/5538927
• Languages: English
• ISSNs: 1076-2787

Abstract

Abstract : With the emergence of deep learning, computer vision has witnessed extensive advancement and has seen immense applications in multiple domains. Specifically, image captioning has become an attractive focal direction for most machine learning experts, which includes the prerequisite of object identification, location, and semantic understanding. In this paper, semantic segmentation and image captioning are comprehensively investigated based on traditional and state-of-the-art methodologies. In this survey, we deliberate on the use of deep learning techniques on the segmentation analysis of both 2D and 3D images using a fully convolutional network and other high-level hierarchical feature extraction methods. First, each domain's preliminaries and concept are described, and then semantic segmentation is discussed alongside its relevant features, available datasets, and evaluation criteria. Also, the semantic information capturing of objects and their attributes is presented in relation to their annotation generation. Finally, analysis of the existing methods, their contributions, and relevance are highlighted, informing the importance of these methods and illuminating a possible research continuation for the application of semantic image segmentation and image captioning approaches.