TRECVID 2007 by the Brno Group

TRECVID 2007 by the Brno Group

conference paper

english

Herout Adam, prof. Ing., Ph.D. (DCGM FIT BUT)
Beran Vítězslav, doc. Ing., Ph.D. (DCGM FIT BUT)
Hradiš Michal, Ing., Ph.D. (DCGM FIT BUT)
Potúček Igor, Ing., Ph.D. (DCGM FIT BUT)
Zemčík Pavel, prof. Dr. Ing. (DCGM FIT BUT)
Chmelař Petr, Ing. (DIFS FIT BUT)

TRECVID 2007, Brno, High Level Feature Extraction, Shot Boundary Detection

High Level Feature Extraction
    1. The runs:
            - A_brU_1 - features extracted from each frame; SVM per-frame classifier trained on frames in each
                shot; simple decision tree judging shots based on per-frame results
            - A_brV_2 - same as A_brU_1, but SVM trained on all training data (the first run divided the training
                data to training and cross-validation datasets), with SVM configured from the previous run
    2. Significant differences between the runs:
            - As expected, the second run performed generally better, in some cases notably better (which is slightly
                surprising, because besides the amount of training data, there was no change)
    3. Contribution of each component:
            - The low-level features appear to be good enough, though their number is relatively large and having
                more time we would experiment with reduction of the feature vector size (now 572 low level features)
            - We considered using some mid-level features based on existing solutions the group has, such as face
                detection, car detection, etc., but for time constraints did not employ these in the feature vector
            - The per-frame classification seems to suffer greatly from mis-annotated frames (whole shots are
                considered to share the same annotation information in our system) and could be the weakest point of
                the system
            - The per-shot decision making seems to be sufficient given the data coming from the per-shot
                classification
    4. Overall comments:
            - see further in the paper

Shot Boundary Detection
We describe our approach to cut detection where we use AdaBoost boosting algorithm to create a detection classifier
from a large set of features which are based on few simple frame distance measures. First, we introduce the reasons
which led us to use AdaBoost algorithm, then we describe the set of features and we also discuss the achieved result.
Finally, we present the possible future improvements to the current approach.

2008

1-6

Proceedings of TRECVID 2007

2007 TRECVID Workshop, NIST, Gaithersburg, Maryland, US

978-1-59593-780-3

National Institute of Standards and Technology

Gaithersburg, US