Abstract:
In this report, a di erent approach for the semantic segmentation of images is
presented. It mainly focuses on the semantic analysis of images that lessen the
gap between semantics and low level visual features of images. Here Convolution
Neural Network(CNN) with deconvolution layers and Conditional Random Fields
(CRFs) is used to get semantic meaning of images. Convolution neural networks
are very powerful visual models which produces the hierarchy of features. Fundamental
principle of these networks is to aggregate the information over larger
image region and collect ne contextual information as the receptive eld of the
image represent. Main motive is to design a model that takes input image of a
xed size and produces output with e cient activation map of the objects appearing
in the image. The model is trained on PASCAL VOC 2007 dataset and getting
70.89% accuracy on training data
