Week 4— Warmth of Image
Title: Weather Condition Prediction from Image
Team Members: Berk GÜLAY, Samet KALKAN, Mert SÜRÜCÜOĞLU
E-mails Respectively: berkgulay.cs@gmail.com , abdulsametkalkan@gmail.com , mertsurucuogluu@gmail.com
Welcome again to our blog post about 4th week of our project . We salute you from a new and promising week in our side.
This week, we could leastwise see about horizon of our project. Eventually we could form/arange and get into use all “Warmth of Image” dataset as we want(cropped/standart , test/validation/train , eliminated and balanced for each class, combined etc.). We took preliminary results from Convolutional Neural Network algorithms using “Keras”. Our image dataset’s cropped part gave approximately %79 accuracy with Convolution layers using Neural Nets.
Architecture for first trial of CNN: 2 convolution layer and a pooling layer, drop-out method usage, 1 fully connected NN for classification part, softmax layer
Used dataset info: 50*50 cropped images from each class (cloudy,sunny,rainy,snowy,foggy) and total 15.000 images approximately.
Preliminary Result of CNN : ~ % 79 accuracy for validation data
Another progress that we also noticed is hardness of snowy view classification. Actually we are trying to use Intensity Histograms(white pixel density) to separate snowy pictures/views but since cloud or sky frames also give similar white pixel dense areas, we could not separate sky segments and snowy areas from each other(check example images below) even if we try different segmentation algorithms(Otsu/Watershed etc.) or hand-crafted tools(Edge Detectors etc.). We are trying to figure out how to separate sky area and snowy parts from each other and determine white pixel density for snowy area segmentation only.
Hopefully we will figure out how to use intensity histograms/white pixel density feature for snowy image detection.
We constructed our feature-set to classify images according to their weather condition type as well. We are planning to use color histogram, brightness, contrast, haze, sharpness, intensity histogram(white pixel density) and sky region metrics as image descriptors for weather condition recognition problem. Moreover, we did not only hypothetically create our feature-set but also we researched related works/projects for our problem and found ways to calculate and measure these metrics combination efficiently.
Lastly we are using and trying different frameworks and tools for our project(like Keras, Libsvm, Scikit-learn, Scikit-image, opencv etc.) so as to determine efficient and beneficial methodologies and algorithms in our problem. Related works and other papers on similar recognition problems help so much while discovering new pathways.
Wait to inform you next weeks and you are always welcome to contact with as about any part of our project. All assistances will be highly appreciated. (“Warmth of Image” Team)
