Abstract
Social media websites such as Flickr and Facebook are pervading our lives today. These
fast-evolving Internet communities are characterized of the presence of large amounts of
images and videos, which has opened up interesting research avenues within the multimedia
and computer vision domains. Social media data is highly interconnected and
heterogeneous and associated with a variety metadata (e.g. HTML tags). In this thesis,
we investigate three important problems in efficient image ranking in the context
of heterogeneous social media networks. We first study the problem of image and tag
co-ranking by utilizing graph structures in image collections and orderless tags. A prototype
of exploring mutually reinforcing relationships between image and tag graphs is
developed which is immediately applicable to image/tag ranking and significantly boosts
the performance compared with previous work. In real-world image search engines, images
from databases are returned ordered by their relevance to the issued query. There is
often significant redundancy in the top-matching images; it would be desirable to remove
the redundancy and present a more diverse range of results, to better cover the search
topic. To address the problem of diversifying image search results, we develop a novel
yet efficient framework, based on non-uniform matroid constraints, to jointly capture
the relevance and diversity. Finally, we study the problem of landmark photo retrieval
over social media networks. Observing that a landmark query issued by a specific user
cannot generally display distinctive landmark features, we develop novel algorithms to
expand the unary query to be a multi-query set over which regular landmark features
can be mined out. An effective landmark specific mid-level representation is presented to support retrieving relevant landmark photos in a scaled way.