A Weighted Multi-Criteria Decision Making Approach for Image Captioning

Maleki Golandouz, Hassan; Ebrahimi Moghaddam, Mohsen; Shamsfard, Mehrnoush

doi:10.48308/jicse.2023.103526

A Weighted Multi-Criteria Decision Making Approach for Image Captioning

Document Type : Original Article

Authors

Hassan Maleki Golandouz ¹

Mohsen Ebrahimi Moghaddam ²

Mehrnoush Shamsfard ³

¹ Faculty of Computer Science and Engineering, Shahid Beheshti University, Tehran, Iran

² Faculty of Computer Science and Engineering, Shahid Beheshti University G.C, Tehran, Iran,

³ Faculty of Computer Science and Engineering, Shahid Beheshti University G.C, Tehran, Iran

https://doi.org/10.48308/jicse.2023.103526

Abstract

Image captioning aims at automatically generating description of an image in natural language. This is a challenging problem in the field of artificial intelligence that has recently received significant attention in the computer vision and natural language processing. Among the existing approaches, visual retrieval based methods have been shown to be highly effective. These approaches search for similar images, then build a caption for the query image based on the captions of the retrieved images. In this study, we present a method for visual retrieval based image captioning, in which we use a multi criteria decision making algorithm to effectively combine several criteria with proportional impact weights to retrieve the most relevant caption for the query image. The main idea of the proposed approach is to design a mechanism to retrieve more semantically relevant captions with the query image and then selecting the most appropriate caption by imitation of the human act based on a weighted multi-criteria decision making algorithm. Experiments conducted on MS COCO benchmark dataset have shown that proposed method provides much more effective results compared to the state-of-the-art models.

Keywords

Image Captioning

Machine Vision

Natural Language Processing

Multi-Criteria Decision Making

Transfer-based Approaches

Subjects