Distance metric learning for multi-modal image retrieval and annotation - Wu, Pengcheng (2014)
With the rapid growth of digital cameras and photo sharing websites, content-based image retrieval (CBIR) and search-based image annotation are important techniques for many real-world multimedia applications. They remain open challenges today, despite being studied extensively for a few decades in several communities, including multimedia, signal processing, and computer vision. One key challenge of CBIR is to find an effective similarity search scheme to accurately retrieve a short list of most similar images from a massive collection of images. The conventional CBIR approaches usually adopt rigid measures to evaluate similarity of images, such as the classical Euclidean distance or cosine similarity, which are often limited despite being widely used in many applications. In this thesis, we investigate Distance Metric Learning (DML) techniques to improve visual similarity search in multimedia information retrieval tasks. In particular, we propose three kinds of novel machine learning algorithms to tackle the challenges of content-based image retrieval and search-based image annotation.