Improving Entity Based Information Retrieval In Video
This PhD by project portfolio advances entity-based information retrieval in video, enabling deep search across multi-modal media in the e-commerce sector. Based in applied industrial research, the resulting work led to several granted technology patents that form a commercial system. The system allows organizations to identify and extract information from video content that was previously challenging to access. The portfolio describes three interconnected projects within a single system designed to extract and provide actionable commercial and industry intelligence at scale. The first project focuses on automatically segmenting and extracting data from webpages, creating a mapping database essential for entity normalization. The second project identifies and classifies key components within video, such as objects, people and brands; these are then aligned across different modalities. Having such topics of interest pre-identified helps reduce the subsequent search space. Finally, the third project details a process for entity normalization through a multi-modal approach to brand and product identification, significantly improving search accuracy. Collectively, these projects enhance brand, logo, sentiment and topic detection, offering a robust foundation for future development in video-based information retrieval.
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