Open AccessImage Captioning and Comparison of Different Encoders
Author(s) -
Ankit Pal,
Subasish Kar,
Anuveksh Taneja,
Vinay Kumar Jadoun
Publication year - 2020
Publication title -
journal of physics. conference series
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.21
H-Index - 85
eISSN - 1742-6596
pISSN - 1742-6588
DOI - 10.1088/1742-6596/1478/1/012004
Subject(s) - closed captioning , computer science , encoder , artificial intelligence , convolutional neural network , sentence , image (mathematics) , artificial neural network , natural language processing , recurrent neural network , natural language , image processing , speech recognition , pattern recognition (psychology) , operating system
Generation of a sentence given an image, called image captioning, has been one of the most intriguing topics in computer vision. It incorporates knowledge of both image processing and natural language processing. Most of the current approaches integrates the concepts of neural network. Different predefined convolutional neural network (CNN) models are used for extracting features from an image and uni-directional or bi-directional recurrent neural network (RNN) for language modelling. This paper discusses about the commonly used models that are used as image encoder, such as Inception-V3, VGG19, VGG16 and InceptionResNetV2 while using the uni-directional LSTMs for the text generation. Further, the comparative analysis of the result has been obtained using the Bilingual Evaluation Understudy (BLEU) score on the Flickr8k dataset.