IMAGE RECOGNITION USING GRAPH BASED CONVOLUTIONAL NETWORK

Abstract

The success behind Deep Learning have mostly relied on Convolutional Neural Network. Convolutional Neural Networks became popular because of their e - cient ability to exploit signi cant statistical properties of images, audio and video data which allows depicting long range interactions in the form of smaller, localized interactions. In Machine Learning, localized feature in the regular domain boosted the use of Convolutional Neural Network, with great advancement in the image processing and classi cation. But there exist some domains such as social networks, bio-informatics data which lack few or all of these fundamental statistical properties and considered as the high-dimensional irregular domain. Being non-trivial in the design and convolution of a kernel lter there arises an issue with the use of Convolution Neural Network within irregular spatial domain. Solution to this problem can be in two direction, where one is to represent these high dimensional irregular domains using graph and then use graph signal processing methods and theorems to execute convolution on graph structure of irregular domain to extract features maps to learnt lters. So, graph convolution and pooling operators like those for regular domain can be a solution to this problem. Other solution to this problem can go in a direction where by calculating gradients on the data input and spectral lter, can achieve deep learning of a problem of irregular spatial domain. Here we will focus on general query of how to build deep networks on non-Euclidean domains in context of spectral theory over graphs with small complexity in its learning. Importantly, the suggested method will o er almost the same constant learning and computational complexity as o ered by standard CNNs and extended to any graph structures. The experiments carried on MNIST and Pascal VOC 2012 datasets depict the ability and e ciency of the proposed deep network to learn the statistical and compositional features of these high-dimensional irregular domains as represented through graphs.