A Deep Learning Framework on Generation of Image Descriptions with Bidirectional Recurrent Neural Networks


Thomas J. J. , Pillai N.

1st International Conference on Intelligent Computing and Optimization (ICO), Pattaya, Thailand, 4 - 05 October 2018, vol.866, pp.219-230 identifier identifier

  • Publication Type: Conference Paper / Full Text
  • Volume: 866
  • Doi Number: 10.1007/978-3-030-00979-3_22
  • City: Pattaya
  • Country: Thailand
  • Page Numbers: pp.219-230
  • Keywords: Deep learning, Convolutional Neural Network, Data augmentation, Malaysian food chain

Abstract

The aim of the paper is to develop a deep learning framework for a model that generates natural descriptions of pictures (data) and their sections so as to search out a lot of insights. Image recognition is one in all the promising applications of visual objects. In this study, a small-scale food image data set consisting of 5115 pictures of fourteen classes and an eight-layer CNN was made to acknowledge these pictures. CNN performed far better with associate degree overall accuracy 54%. The approach influences information sets of images and their patterns bi directional recurrent neural network (BRNN) will concerning the intern-model correspondences between prediction and visual information for calorie estimation. Data expansion techniques were applied to extend the dimensions of trained images, that achieved a considerably improved accuracy of 74% stop the over fitting issue that occurred to the CNN while not misclassified.