Dataset Open Access
Galanopoulos, Damianos; Markatopoulou, Foteini; Mezaris, Vasileios
We provide concept detection scores for the MED16train dataset which is used at the TRECVID Multimedia Event Detection (MED) task . First, each video is decoded into a set of keyframes at fixed temporal intervals (2 keyframes per second). Then, we calculated concept detection scores for the two following concept sets: i) 487 sport-related concepts from YouTube Sports-1M Dataset and ii) 345 TRECVID SIN concepts . The scores have been generated as follows:
1) For the 487 concepts for the Sports-1M Dataset, a Googlenet network  originally trained on 5055 ImageNet concepts was fine-tuned, following the extension strategy of  with one extension layer of dimension 128.
2) For the 345 TRECVID SIN concepts, a pre-trained Googlenet network  on 5055 ImageNet concepts was fine-tuned on these concepts, again following the extension strategy of  with one extension layer of dimension 1024.
After unpacking the compressed file two different folders can be found, namely "Prob_sports_MED16train" and "Prob_SIN_MED16train", one for each concept set. We provide one file for every video of the MED16train dataset for each concept set. Each file consists of N columns (where N = 345 for TRECVID SIN and N = 487 for Sports-1M Dataset) and M rows (where M is the number of extracted keyframes for the corresponding video). Each column corresponds to a different concept, with all concept scores being in the range [0,1]. The higher the score the more likely that the corresponding concept appears in the keyframe. Two additional files are provided; files "sports_487_Classes.txt" and "SIN_345_Classes.txt" indicate the order of the concepts that is used in the concept score files.
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