Scene Labeling using H-LSTM by Predicting the Pixels using Various Functions
N. Shanmugapriya1, D. Chitra2

1N. Shanmugapriya, Assistant Professor, Oxford Engineering College, Trichy, Tmailnadu, India.
Dr. D. Chitra, Professor and Head, Department of Computer Science and Engineering, P.A. College of Engineering and Technology, Pollachi, Tmailnadu, India.
Manuscript received on 1 August 2019. | Revised Manuscript received on 6 August 2019. | Manuscript published on 30 September 2019. | PP: 1179-1185 | Volume-8 Issue-3 September 2019 | Retrieval Number: C4285098319/19©BEIESP | DOI: 10.35940/ijrte.C4285.098319
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Abstract: Scene Labeling plays an important role in Scene understanding in which the pixels are classified and grouped together to form a label of an image. For this concept, so many neural networks are applied and they produce fine results. Without any preprocessing methods, the system works very well compared to methods which are using preprocessing and some graphical models. Here the neural network used to extract the features is Hierarchical LSTM method, which already gives greater result in Scene parsing in the existing method. In order to reduce the computation time and increase the Pixel accuracy H-LSTM is used with Makecform and Softmax functions were applied. The color transformation is applied using the Makecform function. The color enhancement of images has given object as input to H-LSTM function to identify the objects based on the referential shape and color. H-LSTM constructs the neural network by taking the reference pattern and the corresponding label as input. The pixels present in the neighbourhood identified with the help of neural network. In this method, the color image is converted into greyscale and then the Hierarchical LSTM method is applied. Therefore, this method gives greater results when it is implemented in Matlab tool, based on pixel accuracy and computation time when compared to other methods.
Keywords: Scene Labeling, RNN, CNN, LSTM, P-LSTM and MS-LSTM

Scope of the Article: CNN