Summarization Approaches Towards Interpreting Big Visual Data
Finished Project
Abstract The aim of the proposed project is to interpret big and noisy visual data, which has been recorded in diversified environments with no predefined constraints. To this end, the goal is to develop and apply original data mining methods towards extracting important knowledge and increase the accessibility of such archives. Particularly, we aim to focus on summarization approaches, so that the big visual data is more effectively structured and enriched with additional semantic information. The summarization approaches that make use of the multi-modal nature of the data will focus on three main problems: 1) To learn semantic concepts and spatio-temporal attributes from big visual data; 2) organization of large photograph collections; 3) summarization of videos in large web archives. In all these problems, big visual data and the additional information referred as metadata will be handled together.

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