Foldover Features for Dynamic Object Behavior Description in Microscopic Videos
This work addresses behavior analysis for dynamic objects in microscopic videos, which is incremental as it introduces a new feature extraction method for a specific domain.
The paper tackled the problem of describing dynamic object behavior in microscopic videos, particularly for tiny or visually similar objects, by proposing foldover features and achieved a highest classification accuracy of 96.5% on a sperm dataset.
Behavior description is conducive to the analysis of tiny objects, similar objects, objects with weak visual information and objects with similar visual information, playing a fundamental role in the identification and classification of dynamic objects in microscopic videos. To this end, we propose foldover features to describe the behavior of dynamic objects. First, we generate foldover for each object in microscopic videos in X, Y and Z directions, respectively. Then, we extract foldover features from the X, Y and Z directions with statistical methods, respectively. Finally, we use four different classifiers to test the effectiveness of the proposed foldover features. In the experiment, we use a sperm microscopic video dataset to evaluate the proposed foldover features, including three types of 1374 sperms, and obtain the highest classification accuracy of 96.5%.