  While low-level image features have proven to be effective representations
for visual recognition tasks such as object recognition and scene
classification, they are inadequate to capture complex semantic meaning
required to solve high-level visual tasks such as multimedia event detection
and recognition. Recognition or retrieval of events and activities can be
improved if specific discriminative objects are detected in a video sequence.
In this paper, we propose an image representation, called Detection Bank, based
on the detection images from a large number of windowed object detectors where
an image is represented by different statistics derived from these detections.
This representation is extended to video by aggregating the key frame level
image representations through mean and max pooling. We empirically show that it
captures complementary information to state-of-the-art representations such as
Spatial Pyramid Matching and Object Bank. These descriptors combined with our
Detection Bank representation significantly outperforms any of the
representations alone on TRECVID MED 2011 data.
