Object recognition using cellular simultaneous recurrent networks and convolutional neural network

In recent years, Convolutional Neural Networks (CNNs) have become very popular and have achieved great success in many computer vision tasks - particularly in object recognition. Partially inspired by neuroscience, CNNs share many properties with the visual system of the brain. However, the filters of convolutional layers play a vital role on overall accuracy of CNNs. In this paper, the Cellular Simultaneous Recurrent Networks (CSRNs) are applied to generate initial filters of Convolutional Networks (CNs) for features extraction and Regularized Extreme Learning Machines (RELM) are used for classification. Furthermore, Deep Belief Networks (DBN), CNNs with random and Gabor filters are implemented to evaluate the overall performance against the proposed CSRN's filters based CNs with RELM. Experiments were conducted on three popular datasets for object recognition (such as face, pedestrian, and car) to evaluate the performance of the proposed system. The experimental results show that in most of the cases, the proposed approach provides better performance on the extracted features using CSRN's filters with CNs compare to initialize with Gaussian random and DBN for object recognition.