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Multilabel Image Annotation using Multimodal Analysis

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Multilabel Image Annotation using Multimodal Analysis


Pavithra S S | Chitrakala S



Pavithra S S | Chitrakala S "Multilabel Image Annotation using Multimodal Analysis" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-4 | Issue-5, August 2020, pp.1005-1012, URL: https://www.ijtsrd.com/papers/ijtsrd33002.pdf

Image Annotation is one of the most important powerful tools in the field of Computer Vision applications. It has potential application in Face recognition, Robotics, Text recognition, Image retrieval, Image analysis etc. Also, Neural network gains a massive attention in the field of computer science recently. In neural networks, Convolutional neural network (ConvNets or CNNs) is one of the main categories to do images recognition, images classifications, Objects detections, recognition faces etc., are some of the areas where CNNs are widely used. The existing approaches obtain the information cues needed for annotation from Input Images only. This results in lack of context understanding of the post. In order to overcome this issue, Multimodal Image Annotation using Deep Learning (MIADL) approach is proposed. This approach makes use of Multimodal data i.e. Image along with its textual description / content in Automatic Image Annotation. Incorporating Image along with its textual description / content (Multimodal data) gives the better understanding of the context of the post. This will also reduce irrelevant images in image retrieval systems. It is done by using Convolution Neural network to classify and assign multiple labels for the image. It is mainly is for multi-label classification problem that aims at associating a set of textual with an image that describe its semantics. Also using Multimodal data to annotate an Image significantly boost performance than the existing methods.

Neural network, Automatic Image Annotation, Convolution Neural Network (CNN), Part-of-Speech (POS) Tagging, NUS-WIDE dataset, Multimodal, Multilabel


IJTSRD33002
Volume-4 | Issue-5, August 2020
1005-1012
IJTSRD | www.ijtsrd.com | E-ISSN 2456-6470
Copyright © 2019 by author(s) and International Journal of Trend in Scientific Research and Development Journal. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (CC BY 4.0) (http://creativecommons.org/licenses/by/4.0)

International Journal of Trend in Scientific Research and Development - IJTSRD having online ISSN 2456-6470. IJTSRD is a leading Open Access, Peer-Reviewed International Journal which provides rapid publication of your research articles and aims to promote the theory and practice along with knowledge sharing between researchers, developers, engineers, students, and practitioners working in and around the world in many areas like Sciences, Technology, Innovation, Engineering, Agriculture, Management and many more and it is recommended by all Universities, review articles and short communications in all subjects. IJTSRD running an International Journal who are proving quality publication of peer reviewed and refereed international journals from diverse fields that emphasizes new research, development and their applications. IJTSRD provides an online access to exchange your research work, technical notes & surveying results among professionals throughout the world in e-journals. IJTSRD is a fastest growing and dynamic professional organization. The aim of this organization is to provide access not only to world class research resources, but through its professionals aim to bring in a significant transformation in the real of open access journals and online publishing.

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