UMBC ebiquity
Image description using deep neural networks

Image description using deep neural networks

Tim Finin, 8:14am 27 February 2016

Image description using deep neural networks

Sunil Gandhi
10:30 am, Monday, February 29, 2016 ITE 346

With the explosion of image data on the internet, there has been a need for automatic generation of image descriptions. In this project we use deep neural networks for extracting vectors from images and we use them to generate text that describes the image. The model that we built makes use of the pre-trained VGGNET- a model for image classification and a recurrent neural network (RNN) for language modelling. The combination of the two neural networks provides a multimodal embedding between image vectors and word vectors. We trained the model on 8000 images from the Flickr8k dataset and we present our results on test images downloaded from the Internet. We provide a web-service for image description generation that takes the image URL as input and provides image description and image categories as output. Through our service, a user can correct the description automatically generated by the system so that we can improve our model using corrected description.

Sunil Gandhi is a Computer Science Ph.D. student at UMBC who is part of the  Cognition Robotics and Learning Lab (CORAL) research lab.

Comments are closed.