Qualification: 
M.E, MSc
sreekumark@asas.kh.amrita.edu

K. Sreekumar currently serves as Assistant Professor in the Department of Computer Science and I.T., School of Arts & Sciences, Amrita Vishwa Vidyapeetham, Kochi. 

Qualification: M.E. (Computer Science and Engineering), M.Sc.(IT).

Publications

Publication Type: Journal Article

Year of Publication Title

2019

Greeshma K V and K. Sreekumar, “Hyperparameter Optimization and Regularization on Fashion-MNIST Classification”, International Journal of Recent Technology and Engineering (IJRTE), vol. 8, no. 2, 2019.[Abstract]


Nowadays the most exciting technology breakthrough has been the rise of the deep learning. In computer vision Convolutional Neural Networks (CNN or ConvNet) are the default deep learning model used for image classification problems. In these deep network models, feature extraction is figure out by itself and these models tend to perform well with huge amount of samples. Herein we explore the impact of various Hyper-Parameter Optimization (HPO) methods and regularization techniques with deep neural networks on Fashion-MNIST (F-MNIST) dataset which is proposed by Zalando Research. We have proposed deep ConvNet architectures with Data Augmentation and explore the impact of this by configuring the hyperparameters and regularization methods. As deep learning requires a lots of data, the insufficiency of image samples can be expand through various data augmentation methods like Cropping, Rotation, Flipping, and Shifting. The experimental results show impressive results on this new benchmarking dataset F-MNIST.

More »»

2019

Greeshma K. V. and K. Sreekumar, “Fashion-MNIST Classification Based on HOG Feature Descriptor Using SVM”, International Journal of Innovative Technology and Exploring Engineering (IJITEE), vol. 8, no. 5, 2019.[Abstract]


Image recognition and classification plays an important role in many applications, like driverless cars and online shopping. We present the classification of Fashion-MNIST (F-MNIST) dataset using HOG (Histogram of Oriented Gradient) feature descriptor and multiclass SVM (Support Vector Machine). In this paper we explore the impact of one of the successful feature descriptor on Fashion products classification tasks. We have used one of the most simple and effective single feature descriptor HOG. The multiclass SVM which is one of the best machine learning classifier algorithms is used in this method to train the images. Selecting appropriate technique for feature extraction and choosing a best classifier algorithm remains a big challenging task for attaining good classification accuracy. However, the experimental results show that impressive results on this new benchmarking dataset F-MNIST.

More »»