Neural Bag-of-Features learning. (April 2017) - Passalis, Nikolaos; Tefas, Anastasios
Abstract: In this paper, a neural learning architecture for the well-known Bag-of-Features (BoF) model, called Neural Bag-of-Features, is proposed. The Neural BoF model is formulated in two neural layers: a Radial Basis Function (RBF) layer and an accumulation layer. The ability of the Neural BoF model to improve the classification performance is demonstrated using four datasets, including a large-scale dataset, and five different feature types. The gains are two-fold: the classification accuracy increases and, at the same time, smaller networks can be used, reducing the required training and testing time. Furthermore, the Neural BoF natively supports training and classifying from feature streams. This allows the proposed method to efficiently scale to large datasets. The streaming process can also be used to introduce noise and reduce the over-fitting of the network. Finally, the Neural BoF provides a framework that can model and extend the dictionary learning methodology. Abstract : Highlights: A neural generalization of the Bag-of-Features (BoF) model is introduced. The proposed model supports the discriminant weighting of the feature space. Two incremental algorithms (for training and classification) are proposed. A method for providing visual attention information for the BoF model is introduced. The proposed method is evaluated using four datasets from different domains.