Abstract
In this paper, we present our approach used in the TRECVID 2015 Video Hyperlinking Task. Our approach combines text-based similarity calculated on subtitles, visual similarity between keyframes calculated using Feature Signatures, and preference whether the query and retrieved answer come from the same TV series.
All experiments were tuned and tested on about 2500 hours of BBC TV programmes. Our Baseline run exploits fixed-length segmentation, text-based retrieval of subtitles, and query expansion which utilizes metadata, context, in-formation about music and artist contained in the query segment and visual concepts.
The Series run combines the Baseline run with weighting based on information whether the query and data segment come from the same TV series. The FS run combines the Baseline run with the similarity between query and data keyframes calculated using Feature Signatures.
The FSSeriesRerank run is based on the FS run on which we applied reranking which, again, uses information about the TV se