Novel hierarchical Cellular Simultaneous Recurrent neural Network for object detection

Large scale feed forward neural networks have seen intense application in many computer vision problems. However, these networks can get hefty and computationally intensive with increasing complexity of the task. This work, for the first time in literature, introduces a Cellular Simultaneous Recurrent Network (CSRN) based hierarchical neural network for object detection. CSRN has shown to be more effective to solving complex tasks such as maze traversal and image processing when compared to generic feed forward networks. While deep neural networks (DNN) have exhibited excellent performance in object detection and recognition, such hierarchical structure has largely been absent in neural networks with recurrency. This work attempts to introduce deep hierarchy in CSRN for object detection. We propose a novel CSRN based DNN feature extractor that utilizes highly efficient filters derived from CSRNs used in the hidden convolutional layer. Experiments using a face detection task show that the CSRN based DNN offers comparable performance to the state-of-the-art convolutional neural network (CNN), while utilizing only as few as five filters in each of its hidden layers. In comparison, the CNN requires from a few to thousands of filters in each of its hidden layers for the same task. We also demonstrate the flexibility of the proposed architecture by introducing hybridization concept to the network to enhance its scale invariance. The hybrid scale invariant architecture is tested with randomly scaled object image dataset for face detection. Finally, we show that the CSRN based hybrid DNN performance is also comparable to that of the hybrid CNN.