Multi-Object Recognition Using a Feature Descriptor and Neural Classifier

In the field of object recognition, feature descriptors have proven to be able to provide accurate representations of objects facilitating the recognition task. In this sense, Histograms of Oriented Gradients (HOG), a descriptor that uses this approach, together with Support Vector Machines (SVM) have proven to be successful human detection methods. In this paper, we propose a scheme consisting of improved HOG and a classifier with a neural approach to producing a robust system for object recognition. The main contributions of this work are: First, we propose an improved gradient calculation that allows for better discrimination for the classifier system, which consists of performing a threshold over both the magnitude and direction of the gradients. This improvement reduces the rate of false positives. Second, although HOG is particularly suited for human detection, we demonstrate that it can be used to represent different objects accurately, and even perform well in multi-class applications. Third, we show that a classifier that uses a neuronal approach is an excellent complement to a HOG-based feature extractor. Finally, experimental results on the well-known Caltech 101 dataset illustrate the benefits of the proposed scheme