Multiple-instance learning-based sonar image classification

J. Tory Cobb; Xiaoxiao Du; Alina Zare; Matthew Emigh

doi:10.1117/12.2262530

3 May 2017 Multiple-instance learning-based sonar image classification

J. Tory Cobb, Xiaoxiao Du, Alina Zare, Matthew Emigh

Proceedings Volume 10182, Detection and Sensing of Mines, Explosive Objects, and Obscured Targets XXII; 101820H (2017) https://doi.org/10.1117/12.2262530
Event: SPIE Defense + Security, 2017, Anaheim, CA, United States

Abstract

An approach to image labeling by seabed context based on multiple-instance learning via embedded instance selection (MILES) is presented. Sonar images are first segmented into superpixels with associated intensity and texture feature distributions. These superpixels are defined as the "instances" and the sonar images are defined as the "bags" within the MILES classification framework. The intensity feature distributions are discrete while the texture feature distributions are continuous, thus the Cauchy-Schwarz divergence metric is used to embed the instances in a higher-dimensional discriminatory space. Results are given for labeled synthetic aperture sonar (SAS) image database containing images with a variety of seabed textures.

Conference Presentation

Citation Download Citation

J. Tory Cobb, Xiaoxiao Du, Alina Zare, and Matthew Emigh "Multiple-instance learning-based sonar image classification", Proc. SPIE 10182, Detection and Sensing of Mines, Explosive Objects, and Obscured Targets XXII, 101820H (3 May 2017); https://doi.org/10.1117/12.2262530

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