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[SNU Computer Vision Course Project] Image Style Recognition

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Hunjae Jung
Hunjae Jung

This is image style recognition by using CNN

Technologie
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Style Recognition Youngjin Kim, Beomjun Shin, Hunjae Jung 4190.429 Image Processing (2015 Spring) Final Project June 1, 2015
• Why Style Recognition • Environment Settings • Data Sources • Convolutional Neural Network • CNN with Fine-tuning • Hand-crafted Features • Classifier • Conclusion • Demo Outline
Why Style Recognition What do you see in the picture?
4 Why Style Recognition Not only Object, But also Style is important Pastel Noir
5 Why Style Recognition Style contains rich information about image Flickr Style dataset Wikipaintings dataset
Build improved style recognizer that provides rich and vivid information to image Our Mission
Application Example of filtering image search results by style.
Environment Setting Required Hardware Specification GPU with 4GB(≥) memory Affordable Motherboard 600W(≥) Power Supply + @
Environment Setting What we have i3 processor, 2GB memory, GTX 430 graphic card (…)
Environment Setting We bought … GTX 980(4GB) Power(600W) 740 thousand ₩
Environment Setting We bought … GTX 980(4GB) Power(600W) 740 thousand ₩ Our Motherboard was Micro-ATX, So …
Environment Setting We bought … GTX 980(4GB) Power(600W) 740 thousand ₩ We bought … Motherboard, CPU, SSD, Case 690 thousand ₩ Our Motherboard was Micro-ATX, So …
Environment Setting We bought … GTX 980(4GB) Power(600W) 740 thousand ₩ We bought … Motherboard, CPU, SSD, Case 690 thousand ₩ Our Motherboard was Micro-ATX, So … Memory was only 4GB So…
Environment Setting We bought … GTX 980(4GB) Power(600W) 740 thousand ₩ Our Motherboard was Micro-ATX, So … We bought … Motherboard, CPU, SSD, Case 690 thousand ₩ Memory was only 4GB So… We bought … Memory 16GB 128 thousand ₩
Environment Setting We bought … GTX 980(4GB) Power(600W) 740 thousand ₩ We bought … Motherboard, CPU, SSD, Case 690 thousand ₩ We bought … Memory 16GB 128 thousand ₩ Our Motherboard was Micro-ATX, So … Memory was only 4GB So…
Setting Deep Learning Toolbox Keras(ver0.1): Theano-based library Caffe: fastest, nice framework Python, No Configuration file(!) C++, protobuf
Work Flow Classifier pre-training Top5 Score Handcraft Features ConvNet Features Bright Romantic Serene Sunny Macro Color Histogram Color Variance Datasets GIST Descriptor HOG Features
Work Flow Classifier pre-training Top5 Score Handcraft Features ConvNet Features Bright Romantic Serene Sunny Macro Color Histogram GIST Descriptor HOG Features Color Variance Datasets
Data source All labeled 20 styles 60% training, 20% validation, 20% test Positive example : clean Negative example : not clean 80,000 images Curated by Flickr Group[Kerayev. et.al]
Style Labels • Optical techniques : Macro, Depth-of-Field, Long Exposure, HDR • Atmosphere : Hazy, Sunny • Mood : Serene, Melancholy, Ethereal • Composition styles : Minimal, Geometric, Detailed, Texture • Color : Pastel, Bright • Genre : Noir, Vintage, Romantic, Horror
Work Flow Classifier pre-training Top5 Score Handcraft Features ConvNet Features Bright Romantic Serene Sunny Macro Color Histogram Color Variance Datasets GIST Descriptor
Convolutional Neural Network We made theano-based CNN model and tested it too slow, need more data 128 batch, SGD optimize INPUT(256x256) CONV(32,3x3) CONV(32,3x3) MAXPOOLING(4x4) CONV(32,3x3) CONV(32,3x3) MAXPOOLING(4x4) FC(8192-2048) FC(2048-20) SOFTMAX Our CNN Model (keras)
Convolutional Neural Network CaffeNet without fine-tuning too slow, need more data (!) 128 batch, SGD optimize INPUT(256x256) CONV(48,2x2) MAXPOOLING(2x2) CONV(128,3,3) MAXPOOLING(2x2) CONV(192,3x3) CONV(192,3x3) CONV(128,3x3) MAXPOOLING(2x2) FC(10,816-4,096) FC(4,096-4,096) FC(4,096-2000) SOFTMAX CaffeNet (Modified AlexNet)
Convolutional Neural Network No Fine-tuning, No Future Let’s not contribute to global warming 128 batch, SGD optimize INPUT(256x256) CONV(48,2x2) MAXPOOLING(2x2) CONV(128,3,3) MAXPOOLING(2x2) CONV(192,3x3) CONV(192,3x3) CONV(128,3x3) MAXPOOLING(2x2) FC(10,816-4,096) FC(4,096-4,096) FC(4,096-2000) SOFTMAX CaffeNet (Modified AlexNet)
Convolutional Neural Network Fine-tuning CNN (1) Dataset MIT Places Database (2.5 million images, 205 category) Model Deeply Supervised Net (DSN) Deeply-Supervised Nets [Chen-Yu Lee, Saining Xie]
Convolutional Neural Network Fine-tuning CNN (2) Dataset ImageNet (1.2 million images into 1000 category) Model CaffeNet (replication of AlexNet) Caffe: Convolutional Architecture for Fast Feature Embedding [Yangqing Jia, Evan Shelhamer]
Convolutional Neural Network Fine-tuning CNN (3) Dataset ImageNet (1.2 million images into 1000 category) Model VGG 16-layer Net VERY DEEP CONVOLUTIONAL NETWORKS FOR LARGE-SCALE IMAGE RECOGNITION [Karen Simonyan, Andrew Zisserman]
Convolutional Neural Network Winner was VGG, But we used CaffeNet
Work Flow Classifier pre-training Top5 Score Handcraft Features ConvNet Features Bright Romantic Serene Sunny Macro Color Histogram Color Variance Datasets GIST Descriptor
Hand-crafted Features GIST Color Histogram 3 scale, {8, 8, 4} orientation 4 x 4 grid 960 dimension 8 bins, 1 patch for Channel 48 dimension Color Variance 4 x 4 grid for each channel 48 dimension
Concatenate Features CNN GIST Histogram Variance 4,096 960 96 48 1 by 5200 Vector
Work Flow Classifier pre-training Top5 Score Handcraft Features ConvNet Features Bright Romantic Serene Sunny Macro Color Histogram GIST Descriptor Color Variance Datasets
Classifier Linear Classifier 5,200 Feature Vector Hinge Log Squared Hinge Modified Huber Perceptron 0.314 0.309 0.327 0.316 0.303 Input Loss func Accuracy Not enough performance!
Classifier Full-connected Neural Network Let’s tune hyper-parameters
Classifier Varying Number of Hidden layers 1 hidden layer performed better 64 batch, SGD optimize INPUT(5200x1) FC(5152-2048) RELU FC(2048-20) SOFTMAX 1 Hidden Layer 64 batch, SGD optimize INPUT(5200x1) FC(5152-2048) RELU FC(2048-1024) RELU FC(1024-20) SOFTMAX 2 Hidden Layer
Classifier Varying Dropout rate Dropout rate 0.8 is 2% better
Classifier Varying Optimization Techniques All of them overfitted, only SGD survived
Classifier Varying Activation functions RELU shows highest accuracy tanh RELU Sigmoid Soft Plus
Classifier Linear vs Full Connected 31.4 % 30.9 % 32.7 % 31.6 % 30.3 % 39.5 %
Final Touch Now we have every building blocks Any remaining variation? Classifier pre-training Top5 Score Handcraft Features ConvNet Features Bright Romantic Serene Sunny Macro Color Histogram GIST Descriptor Color Variance Datasets
Final Touch Let’s compare combination of features Classifier pre-training Top5 Score Handcraft Features ConvNet Features Bright Romantic Serene Sunny Macro Color Histogram Color Variance Datasets GIST Descriptor
Final Touch Let’s compare combination of features CNN (4,096) GIST Color Histogram Color Variance Acc COMB 1 O O O O 0.3945 COMB 2 O O O 0.3941 COMB 3 O O 0.3949 COMB 4 O 0.3942 Accuracy for various feature combinations CNN GIST 4,096 960 1 by 5,056 Feature Vector Final feature combination Top1 Accuracy. 0.395 > 0.368 (benchmark)
Conclusion Confusion matrix
Conclusion Confusion matrix Vintage vs Pastel Pastel vs Romantic Sunny vs Serene
Conclusion Prediction Accuracy
Conclusion Prediction Accuracy
Demo http://demo.shasta.kr

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