Automatic Image Caption Generation Based on Some Machine Learning Algorithms

Author:

Predić Bratislav1,Manić Daša1,Saračević Muzafer2ORCID,Karabašević Darjan3,Stanujkić Dragiša4

Affiliation:

1. Faculty of Electronic Engineering, University of Niš, Aleksandra Medvedeva 14, Niš 18000, Serbia

2. Department of Computer Sciences, University of Novi Pazar, Dimitrija Tucovića bb, 36300, Novi Pazar, Serbia

3. Faculty of Applied Management, Economics and Finance, University Business Academy in Novi Sad, Belgrade, Serbia, Jevrejska 24, Belgrade 11000, Serbia

4. Technical Faculty in Bor, University of Belgrade, Vojske Jugoslavije 12, Bor 19210, Serbia

Abstract

This paper is dedicated to machine learning, the branches of machine learning, which include the methods for solving this issue, and the practical implementation of the solution to the automatic image description generation. Automatic image caption generation is one of the frequent goals of computer vision. Image description generation models must solve a larger number of complex problems to have this task successfully solved. The objects in the image must be detected and recognized, after which a logical and syntactically correct textual description is generated. For that reason, description generation is a complex problem. It is an extremely important challenge for machine learning algorithms because it represents an impersonation of a complicated human ability to encapsulate huge amounts of highlighted visual pieces of information in descriptive language. The results of the generated descriptions are compared depending on the used pretrained convolutional networks. The BLEU metrics are used to calculate the quality of the image description. Although the solution to the problem of image description automatic generation does provide us with good results, there is yet room for improvement since there are images that are not adequately described.

Publisher

Hindawi Limited

Subject

General Engineering,General Mathematics

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4. Natural Language Processing in Radiology: Update on Clinical Applications;Journal of the American College of Radiology;2022-11

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