TRECVID 2007 by the Brno Group

TRECVID 2007 by the Brno Group

conference paper

English

Herout Adam, prof. Ing., Ph.D. (DCGM)
Beran Vítězslav, doc. Ing., Ph.D. (DCGM)
Hradiš Michal, Ing., Ph.D. (DCGM)
Potúček Igor, Ing., Ph.D.
Zemčík Pavel, prof. Dr. Ing., dr. h. c. (DCGM)
Chmelař Petr, Ing.

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

978-1-59593-780-3

National Institute of Standards and Technology

Gaithersburg