Clustering approaches to feature change detection

Tesfaye G-Michael; Max Gunzburger; Janet Peterson

doi:10.1117/12.2309700

16 May 2018 Clustering approaches to feature change detection

Tesfaye G-Michael, Max Gunzburger, Janet Peterson

Proceedings Volume 10628, Detection and Sensing of Mines, Explosive Objects, and Obscured Targets XXIII; 106281G (2018) https://doi.org/10.1117/12.2309700
Event: SPIE Defense + Security, 2018, Orlando, FL, United States

Abstract

The automated detection of changes occurring between multi-temporal images is of significant importance in a wide range of medical, environmental, safety, as well as many other settings. The usage of k-means clustering is explored as a means for detecting objects added to a scene. The silhouette score for the clustering is used to define the optimal number of clusters that should be used. For simple images having a limited number of colors, new objects can be detected by examining the change between the optimal number of clusters for the original and modified images. For more complex images, new objects may need to be identified by examining the relative areas covered by corresponding clusters in the original and modified images. Which method is preferable depends on the composition and range of colors present in the images. In addition to describing the clustering and change detection methodology of our proposed approach, we provide some simple illustrations of its application.

Citation Download Citation

Tesfaye G-Michael, Max Gunzburger, and Janet Peterson "Clustering approaches to feature change detection", Proc. SPIE 10628, Detection and Sensing of Mines, Explosive Objects, and Obscured Targets XXIII, 106281G (16 May 2018); https://doi.org/10.1117/12.2309700

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