Scene classification using Convolutional Neural Networks - Jayani Withanawasam
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The document discusses scene classification using convolutional neural networks (CNNs). It begins with an outline of the topic, then provides background on computer vision as an AI problem and the importance and challenges of scene classification. It introduces CNNs as a deep learning technique for visual pattern recognition, describing their hierarchical organization and components like convolution and pooling layers. The document also discusses traditional machine learning approaches versus deep learning for scene classification and frameworks like Caffe that can be used to implement CNNs.
Scene classification using Convolutional Neural Networks - Jayani Withanawasam
- 1. Scene Classifica,on using Convolu,onal Neural Networks Jayani Withanawasam
- 2. Outline • Computer vision as an AI problem • Importance of scene classifica,on and its challenges • Tradi,onal machine learning vs. deep learning • Convolu,onal Neural Networks (CNNs) • Using Caffe for implemen,ng CNNs • Important resources to proceed with… 2
- 3. Is this exercise familiar to you? Scene understanding is a primary school task! 3
- 4. What do you see? 4 Photo credits: Kaushalya Madhawa
- 5. What computers see? Source: hSp://www.cs.washington.edu/research/me,p/about/digital.html 5
- 6. Why should we understand visual data? • Billions of views are generated on YouTube on daily basis • In Facebook, hundreds of millions of photo uploads per day Can humans manually process such large volumes of data generated at this rate to instantly find useful insights? 6
- 7. Computer vision as an AI problem • Intelligent behavior of an agent requires the ability to effec,vely interact and manipulate their environment • Detailed understanding of the external environment is achieved using visual percep,on • Computer vision provides methods to analyze images to understand objects and scenes 7
- 8. Using the forest to see the trees! (Torralba et al.) 8 Source: Using the forest to see the trees: exploi,ng context for visual object recogni,on and localiza,on, Torralba et al.)
- 9. Scene classifica,on in computer vision • Main focused areas in computer vision – Computer graphics – Image recogni,on • Image recogni,on is based on concepts related to ar,ficial intelligence and cogni,ve science • Scene classifica,on goes under image recogni,on. • Scene classifica,on problem differs from object recogni,on problem as a scene (context) is composed of mul,ple objects 9
- 10. Scene classifica,on in computer vision (Con,nued.) 10 Source: Srinivasa Narasimhan’s slide
- 11. In 1966, Marvin Minsky at MIT asked his undergraduate student Gerald Jay Sussman to spend the summer linking a camera to a computer and gefng the computer to describe what it saw. We now know the problem is slightly more difficult than that ;) Szeliski 2009, Computer vision 11
- 12. Challenges of scene classifica,on 12 Source: Learning deep features for scene recogni,on using places database, Zhou et al
- 13. Scene classifica,on: then and now Labeling segmenta,ons of the scene (part based models) Analyzing the en,re scene as a whole and train using the available large volumes of data 13
- 14. Deep Learning • Tradi,onal machine learning algorithms, – Do not perform well in high dimensional space – Requires expert knowledge to hand engineer features – High computa,onal cost • Deep learning algorithms, – Specialized form of ar,ficial neural network – Representa,onal learning for high dimensional data – Use of GPUs to accelerate learning
- 15. Inspired by nature… 15 Source: Hubel and Wiesel experiment • Local recep,ve fields • Simple cells • Complex cells
- 16. Convolu,onal Neural Networks (CNNs) • Deep learning technique to recognize spa,al paSerns of data • Hierarchical organiza,on of different abstrac,on levels of image features • Type of Ar,ficial Neural Network (ANN) Assump,on: You are familiar with basic Ar,ficial Neural Networks (ANN) and machine learning concepts 16
- 17. Historical CNN architectures 17 Source: Gradient-‐based learning applied to document recogni,on, LeCun et al, 1998 Source: Imagenet classifica,on with deep convolu,onal neural networks, Krizhevsky et al, 2012
- 18. CNN architecture 18 • Convolu8on layers • Sub-‐sampling (Pooling) layers • Non-‐linearity layers (Ac,va,on func,on) • Fully connected (FC) layer (op,onal) Source: hSps://adeshpande3.github.io/adeshpande3.github.io/A-‐Beginner's-‐Guide-‐To-‐Understanding-‐Convolu,onal-‐Neural-‐Networks/
- 19. Important hyper parameters for CNN • Number of filters (kernals) • Stride • Size of the filter • Amount of padding • Other (not CNN specific) – Learning rate (and its decay) – Batch size – Momentum 19
- 20. Caffe for CNN implementa,on • Convolu,onal Architecture For Feature Extrac,on • Deep learning framework by Berkley Vision and Learning center hSp://caffe.berkeleyvision.org/ • Reference models in Caffe model Zoo • Input (E.g., lmdb) • Net: Layers (data, loss, convolu,on) E.g., lenet_train.prototxt • Solver (learning rate, net, model snapshots, valida,on) E.g., lenet_solver.prototxt 20
- 22. lenet_train.prototxt (few important layers) 22 Data layer Pooling layer Convolu,onal layer
- 23. MIT Places for scene recogni,on • MIT Places database • Places2 Challenge • MIT Scene Recogni,on Demo • hSp://places.csail.mit.edu 23
- 24. Important resources • CS231n: Convolu,onal neural networks for visual recogni,on, Fei Fei Li, Andrej Karpathy, Jus,n Johnson, Stanford university. hSp://cs231n.stanford.edu/ • DeepLearninbook, Ian Goodfellow, Yoshua Bengio, Aaron Courville. hSp://www.deeplearningbook.org/ 24
- 25. We are not there yet… Source: Concise Computer Vision 25
- 26. Contact me • Linkedin: hSps://www.linkedin.com/in/ jayaniwithanawasam • Email: [email protected] 26
- 27. Thank you 27