出版社：Academy & Industry Research Collaboration Center (AIRCC)
摘要：In this digital world, artificial intelligence has provided solutions to many problems, likewise toencounter problems related to digital images and operations related to the extensive set ofimages. We should learn how to analyze an image, and for that, we need feature extraction ofthe content of that image. Image description methods involve natural language processing andconcepts of computer vision. The purpose of this work is to provide an efficient and accurateimage description of an unknown image by using deep learning methods. We propose a novelgenerative robust model that trains a Deep Neural Network to learn about image features afterextracting information about the content of images, for that we used the novel combination ofCNN and LSTM. We trained our model on MSCOCO dataset, which provides set of annotationsfor a particular image, and after the model is fully automated, we tested it by providing rawimages. And also several experiments are performed to check efficiency and robustness of thesystem, for that we have calculated BLUE Score.
关键词：Image Annotation; Feature Extraction; LSTM; Deep Learning; NLP.
