Automatic Generation of Description for Images Using Recurrent Neural Network

被引：0

作者：

Veena, G. S. ^{[1
]}

Patil, Savitri ^{[1
]}

Kumar, T. N. R. ^{[1
]}

机构：

[1] Ramaiah Inst Technol, Bengaluru, India

来源：

COMPUTING AND NETWORK SUSTAINABILITY | 2019年 / 75卷

关键词：

Artificial intelligence; Consciousness; Deep learning; Intelligent agent; Long short-term memory; InceptionV3; Flickr30k; Bilingual evaluation understudy;

D O I：

10.1007/978-981-13-7150-9_44

中图分类号：

TP3 [计算技术、计算机技术];

学科分类号：

0812 ;

摘要：

In this paper, a generative long short-term memory (LSTM) model for generating description of the image is implemented. Automatic generation of description that describes the content of a given image is a fundamental problem in artificial intelligence. This kind of work is achieved by connecting two different domains like computer vision and natural language processing. The solution proposed here makes use of deep learning. A deep learning framework known as Keras is used which uses TensorFlow for the backend process. TensorFlow is a framework used to do a series of operations in a chain. The general technique is to feed the features of an image to the model, which is capable of generating text of length less than or equal to a predefined caption length. The dataset Flickr30 K is used to train the model. The InceptionV3 is used to extract features of the images. BLEU metric is used to measure the accuracy of the description that is generated for that image using LSTM model.

引用

页数：11

共 50 条

[41] AUTOMATIC SEGMENTATION OF LIVER STRUCTURE IN CT IMAGES USING A NEURAL-NETWORK
TSAI, DY
IEICE TRANSACTIONS ON FUNDAMENTALS OF ELECTRONICS COMMUNICATIONS AND COMPUTER SCIENCES, 1994, E77A (11) : 1892 - 1895
[42] Automatic classification of brain MRI images using SVM and neural network classifiers
Department of Computer Science and Engineering, College of Engineering Guindy, Anna University, Chennai, India
Adv. Intell. Sys. Comput., (2015-2019): : 2015 - 2019
[43] Automatic classification of urinary sediment images by using a hierarchical modular neural network
Mitsuyama, S
Motoike, J
Matsuo, H
MEDICAL IMAGING 1999: IMAGE PROCESSING, PTS 1 AND 2, 1999, 3661 : 680 - 688
[44] DRAW: A Recurrent Neural Network For Image Generation
Gregor, Karol
Danihelka, Ivo
Graves, Alex
Rezende, Danilo Jimenez
Wierstra, Daan
INTERNATIONAL CONFERENCE ON MACHINE LEARNING, VOL 37, 2015, 37 : 1462 - 1471
[45] AUTOMATIC TRIMAP GENERATION BY A MULTIMODAL NEURAL NETWORK
Taniguchi, Masaki
Tezuka, Taro
2021 IEEE INTERNATIONAL CONFERENCE ON IMAGE PROCESSING (ICIP), 2021, : 2768 - 2772
[46] Recurrent neural networks for automatic clustering of multispectral satellite images
Koprinkova-Hristova, P.
Alexiev, K.
Borisova, D.
Jelev, G.
Atanassov, V.
IMAGE AND SIGNAL PROCESSING FOR REMOTE SENSING XIX, 2013, 8892
[47] Automatic cardiac landmark localization by a recurrent neural network
van Zon, Mike
Veta, Mitko
Li, Shuo
MEDICAL IMAGING 2019: IMAGE PROCESSING, 2019, 10949
[48] Automatic Test Data Generation for a Given Set of Applications Using Recurrent Neural Networks
Paduraru, Ciprian
Melemciuc, Marius-Constantin
Paduraru, Miruna
SOFTWARE TECHNOLOGIES, ICSOFT 2018, 2019, 1077 : 307 - 326
[49] A Deep Dive into Automatic Code Generation Using Character Based Recurrent Neural Networks
Priya, Renita
Wang, Xinyuan
Sun, Yu
Hu, Yujie
PROCEEDINGS 2017 INTERNATIONAL CONFERENCE ON COMPUTATIONAL SCIENCE AND COMPUTATIONAL INTELLIGENCE (CSCI), 2017, : 369 - 374
[50] Automatic Speech Recognition trained with Convolutional Neural Network and predicted with Recurrent Neural Network
Soundarya, M.
Karthikeyan, P. R.
Thangarasu, Gunasekar
2023 9TH INTERNATIONAL CONFERENCE ON ELECTRICAL ENERGY SYSTEMS, ICEES, 2023, : 41 - 45

← 1 2 3 4 5 →