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.

Links:

Citation:

J. T. Cobb, X. Du, A. Zare and M. Emigh, "Multiple-instance learning-based sonar image classification," in Proc. SPIE 10182, Detection and Sensing of Mines, Explosive Objects, and Obscured Targets XXII, 2017. 

@InProceedings{Cobb2017Multiple,
Title = {Multiple-instance learning-based sonar image classification},
Author = {Cobb, James T. and Du, Xiaoxiao and Zare, Alina and Emigh, Matthew},
Booktitle = {Proc. SPIE 10182, Detection and Sensing of Mines, Explosive Objects, and Obscured Targets XXII},
Year = {2017}
}