Abstract

The sky is an essential component in outdoor images. Sky and cloud type classification has applications in many areas such as image enhancement and sky image retrieval. In this paper, we improve the sky and cloud type classification rate over existing methods. Our work is based on two classification stages: sky image classification stage and sky cloud type classification stage. In sky classification stage, the image is classified into blue sky, cloudy sky, and sunset sky. Due to the impact of descriptor selection in the sky classification, we investigate ten descriptors; we show that the classifiers based on color descriptors are more accurate than the classifiers based on shape descriptors in sky type classification. We improve the sky image classification ratio using K-HSV descriptors. The sky classification with K-HSV descriptors has 77.3% correct classification rate. In cloud type's classification stage, the cloud is classified based on the sky type. For both the blue sky and the sunset sky, the cloud type is classified into six types: cloudless, thin-cirrus, cirrus, cirrocumulus, cumulus, and cumulonimbus. In cloudy sky, the cloud type is classified into three types: stratus, stratocumulus, and altostratus. The clouds are classified based on their shape and color using Gist minimum distance classification. The average correct classification rate of the clouds classifier is over 85% for cloudless, cumulus clouds, and stratus clouds and over 60% for thin-cirrus, cumulonimbus, stratocumulus, and altostratus clouds.

Highlights

  • Image classification is one of the major challenges to the computer vision community

  • We searched for sky images using keywords “sky”, “cloudy sky”, “blue sky”, "sunset", and "cloud types". 1200 images are used in training the sky type classifier

  • We study some of the descriptors that are used in bag-of-words image classification

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Summary

Introduction

Image classification is one of the major challenges to the computer vision community. Scene classification is used in many applications such as content-based indexing and organization, and content-sensitive image enhancement. Two classifier techniques are used in image classification: minimum distance classifier(4) and bag-of-words classifier(5). The distance is defined as an index of similarity. Bag-of-words is the application of local features in image classification that inspires and initiates many research efforts(6). The selection of descriptors plays an important role in the bag of visual words classification. The scene classification approaches are used for well defined shapes (rigid) classes such as indooroutdoor(6, 7) and cars-airplanes(8, 9). These classifiers are using descriptors that depend mainly on shape like the SIFT descriptor

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