Part-Based Feature Aggregation Method for Dynamic Scene Recognition

Existing methods for dynamic scene recognition mostly use global features extracted from the entire video frame or a video segment. In this paper, a part-based method is proposed for aggregating local features from multiple video frames. A pre-trained Fast R-CNN model is used to extract local convolutional layer features from the regions of interest (ROIs) of training images. These features are then clustered to locate representative parts. A set cover problem is formulated to select the discriminative parts, which are further refined by fine-tuning the Fast R-CNN. Local convolutional layer features and fully-connected layer features are extracted using the fine-tuned Fast R-CNN model, and then aggregated separately from a video segment to form two feature representations. They are concatenated into a global feature representation. Experimental results show that the proposed method outperforms several state-of-the-art features on two dynamic scene datasets.