Towards an Architecture for Efficient Distributed Search of Multimodal Information

The creation of very large-scale multimedia search engines, with more than one billion images and videos, is a pressing need of digital societies where data is generated by multiple connected devices. Distributing search indexes in cloud environments is the inevitable solution to deal with the increasing scale of image and video collections. The distribution of such indexes in this setting raises multiple challenges such as the even partitioning of data space, load balancing across index nodes and the fusion of the results computed over multiple nodes. The main question behind this thesis is how to reduce and distribute the multimedia retrieval computational complexity? This thesis studies the extension of sparse hash inverted indexing to distributed settings. The main goal is to ensure that indexes are uniformly distributed across computing nodes while keeping similar documents on the same nodes. Load balancing is performed at both node and index level, to guarantee that the retrieval process is not delayed by nodes that have to inspect larger subsets of the index. Multimodal search requires the combination of the search results from individual modalities and document features. This thesis studies rank fusion techniques focused on reducing complexity by automatically selecting only the features that improve retrieval effectiveness. The achievements of this thesis span both distributed indexing and rank fusion research. Experiments across multiple datasets show that sparse hashes can be used to distribute documents and queries across index entries in a balanced and redundant manner across nodes. Rank fusion results show that is possible to reduce retrieval complexity and improve efficiency by searching only a subset of the feature indexes