Content-based binary indexing of coral reef stitched images

Authors

  • Micholo Lanz B. Medrana National Institute of Physics, University of the Philippines Diliman
  • Maricor N. Soriano National Institute of Physics, University of the Philippines Diliman

Abstract

The categorization and searching of images based on content is a fundamental part of image data analysis, but may prove tedious if done manually. Automating a task to separate images with relevant features of study may prove a useful tool in the research on large image datasets. This paper proposes a method for content-based indexing of underwater coral reef stitched images acquired by the Teardrop system into feature and non-feature classes by means of grey-level co-occurrence matrix (GLCM) features and a k-nearest neighbor classifier with a k-value of 5. Results of classification on three sites showed accuracy (ACC) values ranging from 80 to 90% when compared with the ground truth, while the average of both negative predictive value (NPV) and true positive rate (TPR) values were seen at 72.01 %. A total processing time of 1.48 minutes/100 images on average was achieved. Further research may improve the classifier through different classification methods or extend classification into higher-level semantic categories beyond the original binary classification

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Issue

Article ID

SPP-2016-4C-04

Section

Instrumentation, Imaging, and Signal Processing

Published

2016-08-18

How to Cite

[1]
MLB Medrana and MN Soriano, Content-based binary indexing of coral reef stitched images, Proceedings of the Samahang Pisika ng Pilipinas 34, SPP-2016-4C-04 (2016). URL: https://proceedings.spp-online.org/article/view/SPP-2016-4C-04.