Abstract
During the last few years, object recognition has received a big of interest in an attempt to make use of the large scale image datasets. Object recognition allows understanding image based on the objects that it contains. The success of the deep learning based methods in recognizing objects has encouraged recent works to follow this approach. In this paper, we propose a Boosted Convolutional Neural Network approach for object recognition. Our approach uses a very deep convolutional neural network reinforced by adding Boosted Blocks. These Boosted Blocks consist of a succession of convolutional layers boosted by using a Multi-Bias Nonlinear Activation function, that enriches the expressive power of the network, as well as a Concatenated Rectified Linear Unit function which ensures the preservation of all the information after the convolutional layer. Besides, inspired by the visual system of the human brain, our Boosted Convolutional Layer is designed following a recurrent structure. In addition, rather than using the classical max-pooling, our Boosted Convolutional Neural Network is improved by applying Generalizing pooling which allows pooling to adapt to complex and variable patterns. Furthermore, the Spatial Pyramid Pooling after the last Boosted Block has been conducted after the Boosted Block in order to remove the fixed-size constraint of input image. Our approach is evaluated on four different object recognition benchmarks: Pascal VOC 2007, Pascal VOC 2012, CIFAR-10 and the larger dataset ILSVRC-2012. These datasets are widely used by the recent object recognition methods. The experimental results validate the efficiency of our method in comparison with other methods from the literature.