Features to Text: A Comprehensive Survey of Deep Learning on Semantic Segmentation and Image Captioning

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 identif...

Full description

Saved in:
Bibliographic Details
Published in:Complexity (New York, N.Y.) N.Y.), 2021, Vol.2021 (1)
Main Authors: Oluwasammi, Ariyo, Aftab, Muhammad Umar, Qin, Zhiguang, Ngo, Son Tung, Doan, Thang Van, Nguyen, Son Ba, Nguyen, Son Hoang, Nguyen, Giang Hoang
Format: Article
Language:English
Subjects:
Algorithms
Artificial intelligence
Clustering
Computer vision
Datasets
Deep learning
Domains
Feature extraction
Image annotation
Image segmentation
Machine learning
Natural language processing
Neural networks
Semantic segmentation
Semantics
Support vector machines
Two dimensional analysis
Citations: Items that this one cites
Items that cite this one
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary: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.
ISSN:1076-2787
1099-0526
DOI:10.1155/2021/5538927