Title:

High-level and Low-level Feature Set for Image Caption Generation with Optimized Convolutional Neural Network, Journal of Telecommunications and Information Technology, 2022, nr 4

Creator:

Kalla, Mukesh ; Sharma, Arvind ; Padate, Roshni ; Jain, Amit

Subject and Keywords:

CNN ; SMO-SCME algorithm ; sharpness ; proposed contrast ; image caption

Description:

Automatic creation of image descriptions, i.e. captioning of images, is an important topic in artificial intelligence (AI) that bridges the gap between computer vision (CV) and natural language processing (NLP). Currently, neural networks are becoming increasingly popular in captioning images and researchers are looking for more efficient models for CV and sequence-sequence systems. This study focuses on a new image caption generation model that is divided into two stages. Initially, low-level features, such as contrast, sharpness, color and their high-level counterparts, such as motion and facial impact score, are extracted. Then, an optimized convolutional neural network (CNN) is harnessed to generate the captions from images. To enhance the accuracy of the process, the weights of CNN are optimally tuned via spider monkey optimization with sine chaotic map evaluation (SMO-SCME). The development of the proposed method is evaluated with a diversity of metrics.

Publisher:

Instytut Łączności - Państwowy Instytut Badawczy

Date:

2022, nr 4

Resource Type:

artykuł

Format:

application/pdf

Resource Identifier:

ISSN 1509-4553, on-line: ISSN 1899-8852

DOI:

10.26636/jtit.2022.164222

eISSN:

1899-8852

Source:

Journal of Telecommunications and Information Technology

Language:

ang

Rights Management:

Biblioteka Naukowa Instytutu Łączności