A Weighted Multi-Criteria Decision Making Approach for Image Captioning

TL;DR

This paper introduces a weighted multi-criteria decision making approach for image captioning that improves retrieval relevance by combining multiple criteria with impact weights, outperforming existing models on MS COCO.

Contribution

It presents a novel visual retrieval based image captioning method using a weighted multi-criteria decision making algorithm for better caption relevance.

Findings

Outperforms state-of-the-art models on MS COCO dataset

Effectively combines multiple criteria with impact weights

Enhances semantic relevance of retrieved captions

Abstract

Image captioning aims at automatically generating descriptions 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 proven 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 in compare to the state-of-the-art models by using criteria with proportional impact weights .