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Image classification using deep learning Project Proposal, Study Guides, Projects, Research of Information Technology

University of Mumbai Information Technology

Image classification is where a computer can analyze an image and identify the ‘class’ the image falls under. (Or a probability of the image being part of a ‘class’.) A class is essentially a label, for instance, ‘car’, ‘animal’, ‘building’ and so on. For example, you input an image of a sheep. Image classification is the process of the computer analyzing the image and telling you it’s a sheep. (Or the probability that it’s a sheep.) Early image classification relied on raw pixel data. This mea

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Image classification using deep learning

A Project Proposal on

“Image classification using deep learning”

Submitted by

Prathmesh Deshmukh – 2020B0042

Manoj Thamke – 20202B0044

Pranay Mahajan – 20202B0055

Soham Korde – 20202B0056

Head of the Department

Mrs. Yogita Jore

Department of Information Technology

(NBA Accredited)

Vidyalankar Polytechnic

Wadala (E), Mumbai – 400 037

Maharashtra State Board of Technical Education, Mumbai

2022 – 23

On special offer

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A Project Proposal on “Image classification using deep learning” Submitted by Prathmesh Deshmukh – 2020B Manoj Thamke – 20202B Pranay Mahajan – 20202B Soham Korde – 20202B Head of the Department Mrs. Yogita Jore Department of Information Technology (NBA Accredited) Vidyalankar Polytechnic Wadala (E), Mumbai – 400 037 Maharashtra State Board of Technical Education, Mumbai

Abstract

Image classification is where a computer can analyze an image and identify the

‘class’ the image falls under. (Or a probability of the image being part of a ‘class’.)

A class is essentially a label, for instance, ‘car’, ‘animal’, ‘building’ and so on.

For example, you input an image of a sheep. Image classification is the process of

the computer analyzing the image and telling you it’s a sheep. (Or the probability

that it’s a sheep.)

Early image classification relied on raw pixel data. This meant that computers

would break down images into individual pixels. The problem is that two pictures of

the same thing can look very different. They can have different backgrounds,

angles, poses, etcetera. This made it quite the challenge for computers to correctly

‘see’ and categorize images.

Deep learning is a type of machine learning; a subset of artificial intelligence (AI)

that allows machines to learn from data. Deep learning involves the use of computer

systems known as neural networks.

In neural networks, the input filters through hidden layers of nodes. These nodes

each process the input and communicate their results to the next layer of nodes. This

repeats until it reaches an output layer, and the machine provides its answer.

There are different types of neural networks based on how the hidden layers work.

Image classification with deep learning most often involves convolutional neural

networks, or CNNs. In CNNs, the nodes in the hidden layers don’t always share

their output with every node in the next layer (known as convolutional layers).

Deep learning allows machines to identify and extract features from images. This

means they can learn the features to look for in images by analyzing lots of pictures.

So, programmers don’t need to enter these filters by hand.

II. Convolutional neural network

2. Flow chart / Block Diagram (if any needed to explain the topic) Flow Chart

4. Technologies used for the project  Python  TensorFlow Library  CUDA Library  Convolutional Neural Network  Artificial Neural Network  VScode  Spyder  Jupyter notebook

5. Reference Paper (in any) [1] https://in.mathworks.com/matlabcentral/fileexchange/59133-neural-network-toolbox-tm-- model-for-alexnet-network [2] H. Lee, R. Grosse, R. Ranganath, and A.Y. Ng. Convolutional deep belief networks for scalable unsupervised learning of hierarchical representations. In Proceedings of the 26th Annual International Conference on Machine Learning, pages 609–616. ACM, 2009 [3] Deep Learning with MATLAB – MATLAB expo [4] Introducing Deep Learning with the MATLAB – Deep Learning E-Book provided by the MathWorks. [5] KISHORE, P.V.V., KISHORE, S.R.C. and PRASAD, M.V.D., 2013. Conglomeration of hand shapes and texture information for recognizing gestures of Indian sign language using feed forward neural networks. International Journal of Engineering and Tech- nology, 5(5), pp. 3742-3756. [6] RAMKIRAN, D.S., MADHAV, B.T.P., PRASANTH, A.M., HARSHA, N.S., VARDHAN, V., AVINASH, K., CHAITANYA, M.N. and NAGASAI, U.S., 2015. Novel compact asymmetrical fractal aperture Notch band antenna. Leonardo Electronic Journalof Practices and Technologies, 14(27), pp. 1-12. [7] KARTHIK, G.V.S., FATHIMA, S.Y., RAHMAN, M.Z.U., AHAMED, S.R. and LAY- EKUAKILLE, A., 2013. Efficient sig- nal conditioning techniques for brain activity in remote health monitoring network. IEEE Sensors Journal, 13(9), pp. 3273-3283. [8] KISHORE, P.V.V., PRASAD, M.V.D., PRASAD, C.R. and RA- HUL, R., 2015. 4-Camera model for sign language recognition using elliptical Fourier descriptors and ANN, International Conference on Signal Processing and Communication Engineering Systems - Proceedings of SPACES 2015, in Association with IEEE 2015, pp. 34-38. [9] LeCun, Y., Bottou, L., Bengio, Y., &Haffner, P. (1998) “Gradient-based learning applied to document recognition.” proceedings of the IEEE 86(11): 2278- 2324. [10] Srivastava, N., Hinton, G. E., Krizhevsky, A., Sutskever, I., &Salakhutdinov, R. (2014) “Dropout: a simple way to prevent neural networks from overfitting. Journal of machine learning research 15(1): 1929-1958.

6. Letter of Project Allocation (in case of Industry Project)