DLDR: Deep Linear Discriminative Retrieval for Cultural Event Classification From a Single Image

Rasmus Rothe, Radu Timofte, Luc Van Gool; Proceedings of the IEEE International Conference on Computer Vision (ICCV) Workshops, 2015, pp. 53-60

In this paper we tackle the classification of cultural events from a single image with a deep learning based method. We use convolutional neural networks (CNNs) with VGG-16 architecture, pretrained on ImageNet or the Places205 dataset for image classification, and fine-tuned on cultural events data. CNN features are robustly extracted at 4 different layers in each image. At each layer Linear Discriminant Analysis (LDA) is employed for discriminative dimensionality reduction. An image is represented by the concatenated LDA-projected features from all layers or by the concatenation of CNN pooled features at each layer. The classification is then performed through the Iterative Nearest Neighbors-based Classifier (INNC). Classification scores are obtained for different image representation setups at train and test. The average of the scores is the output of our deep linear discriminative retrieval (DLDR) system. With 0.80 mean average precision (mAP) DLDR is a top entry for the ChaLearn LAP 2015 cultural event recognition challenge.