Deep Learning for Image Understanding

Computer vision and image understanding is the problem of interpreting images by locating, recognizing objects, attributes and other higher level features in an image. In this thesis, I seek to tackle this broad problem using deep learning techniques. More specifically, I build deep neural network based models to solve two specific problems to understand images in a high level: album wise image understanding with event-specific image importance score, and description generation for an image. I first focus on the understanding of a collection of images in an event album. In an event album, some images are more important or interesting to save or present than others, and I show that with an event-specific image importance property, we can learn the interestingness of an image given an album, and the performance of the model generated importance score is very close to human preference. I build a siamese network that can predict image importance score given the event type of that image, using novel objective function and learning scheme. Next, to make the process fully automated, I propose an iterative updating procedure for event type and image importance score prediction, that can simultaneously decide the event type of the album and the importance score of every image. It consists of a Convolutional Neural Network that recognizes the event type, a Long-Short Term Memory (LSTM) that uses sequential information for event type recognition, and a siamese network that predicts image importance score. Furthermore, not just limited to describing an image with a score or by a classified type, I seek the possibility to describe it with a phrase or sentence. I propose a coarse-to-fine LSTM based method that decomposes the original image description into a skeleton sentence and its notable attributes, and demonstrate that in this way the language model can generate better descriptions, with the capability to generate image descriptions that better accommodates user preference