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Semantic Rules-Based Classification of Outdoor Natural Scene Images

  • C. A. LaulkarEmail author
  • P. J. Kulkarni
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 1025)

Abstract

This paper proposes the classification of outdoor natural scene images using semantic rules. The proposed work is divided into three stages: segmentation, object recognition and image classification. Segmented images are generated by applying SPDBSCAN with user interaction on original image. CNN model is trained of using these segmented images for recognition of the objects such as sky, water, green land and sand. Semantic rules are designed using information of object class and its spatial location in an image for classification of scene image into either of four classes: green_ground, desert, sea_water and beach class. For this research work, we have used images from SUN-397 dataset. The work has achieved F-ratio of 84% for scene classification.

Keywords

Image classification Segmentation SPDBSCAN CNN AlexNet Object recognition Semantic rules 

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Copyright information

© Springer Nature Singapore Pte Ltd. 2020

Authors and Affiliations

  1. 1.Computer Science and EngineeringWalchand College of EngineeringSangliIndia

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