Exploring deep features: deeper fully convolutional neural network for image segmentation

Abstract

Classification of images has been a widely regarded challenge for the past decade, but a new type of object recognition problem which deals with pixellevel segmentation is posing a more complex task for both computer vision enthusiasts and researcher alike. The convolutional neural network has become a staple for any recognition task, but a new type of ConvNet which is Fully convolutional in architecture has yielded more fine features and proponents. We propose a neural net where we take VGG19 [20], a well-known classification CNN, make it fully convolutional for extracting deeper features and lastly use skip-architectures[15] for getting finer output. This yields better result than the pre-existing FCN segmentation architecture [15, 25, 6]. Training was done on augmented VOC12 [4] with SBD [6]training data and validation set was used from reduced VOC12 validation dataset. The model scored mIOU of 68.1 percent in PASCAL VOC 2012 Segmentation challenge.