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Multilabel Image Annotation using Multimodal Analysis

Journal: International Journal of Trend in Scientific Research and Development (Vol.4, No. 5)

Publication Date:

Authors : ;

Page : 1005-1012

Keywords : Data Miining; Neural network; Automatic Image Annotation; Convolution Neural Network (CNN); Part-of-Speech (POS) Tagging; NUS-WIDE dataset; Multimodal; Multilabel;

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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. 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, URL: Paper Url :

Last modified: 2020-10-03 17:38:24