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Abstract Features learned by deep Convolutional Neural Networks (CNNs) have been recognized to be more robust and expressive than hand-crafted ones. They have been successfully used in different computer vision tasks such as object detection, pattern recognition and image understanding. Given a CNN architecture and a training procedure, the efficacy of the learned features depends on the domain-representativeness of the training examples. In this paper we investigate the use of CNN-based features for the purpose of food recognition and retrieval. To this end, we first introduce the Food-475 database, that is the largest publicly available food database with 475 food classes and 247,636 images obtained by merging four publicly available food databases. We then define the food-domain representativeness of different food databases in terms of the total number of images, number of classes of the domain and number of examples for class. Different features are then extracted from a CNN based on the Residual Network with 50 layers architecture and trained on food databases with diverse food-domain representativeness. We evaluate these features for the tasks of food classification and retrieval. Results demonstrate that the features extracted from the Food-475 database outperform the other ones showing that we need larger food databases in order to tackle the challenges in food recognition, and that the created database is a step forward toward this end. Highlights • CNN-based features are used for the purpose of food classification and retrieval. • Several CNN architectures are compared and Residual Networks (ResNet-50) are the best. • Fine-tuned and trained ResNet-50 are used as feature extractors for food recognition. • A new benchmark food database which contains 475 food classes is introduced. • Our results show that the most robust features for food classification and retrieval are those obtained from the ResNet-50 fine-tuned on the Food-475 database. [ABSTRACT FROM AUTHOR]

Highlights • The hand-crafted features and the deep features were combined into a deep network. • The importance of different body parts were revealed by the gait descriptor. • The body parts were described in more details after updating data with weights. Gait recognition is an important issue currently. In this paper, we propose to combine deep features and hand-crafted representations into a globally trainable deep model. Specifically, a set of deep feature vectors are firstly extracted by a pre-trained CNN model from the input sequences. Then, a kernel function with respect to the fully connected vector is trained as the guiding weight of the respective receptive fields of the input sequences. Therefore, the hand-crafted features are extracted based on the guiding weight. Finally, the hand-crafted features and the deep features are combined into a unified deep network to complete classification. The optimized gait descriptor, termed as deep convolutional location weight descriptor (DLWD), is capable of effectively revealing the importance of different body parts to gait recognition accuracy. Experiments on two gait data sets (i.e., CASIA-B, OU-ISIR) show that our method outperforms the other existing methods for gait recognition. [ABSTRACT FROM AUTHOR]