Saliency-based classification of objects in unconstrained underwater environments

Abstract

Exploration of the deep-sea underwater environment is a challenging and non-trivial task. Underwater vehicles used for the exploration of such environments capture videos continuously. The processing of these videos is a major bottleneck for scientific research in this area. This paper presents a methodology for the classification of the objects in the unconstrained underwater environments into two broad classes namely - man-made and natural. The classification of the objects is achieved using the saliency gradient based morphological active contour models. A bag of features acquired from the contours of the objects is used for the classification using various classifiers. Principal Component Analysis is used for the removal of redundancy in the feature set. The proposed features classify the objects present in the unconstrained underwater environment into a man-made/natural class using the proposed features. The results show that all the classifiers performed well; though KNN and ensemble subspace KNN, performed marginally better.