A New SVM-Based Architecture for Object Recognition in Color Underwater Images with Classification Refinement by Shape Descriptors

Underwater images analysis is a difficult task due to their specific attributes: weak and variable lighting, low contrast, blurring. Therefore powerful image analysis algorithms, application specific, must be employed to obtain good results. In this paper we propose such a novel architecture based on a support vector machine (SVM) classifier, dedicated to large underwater scenes analysis for the specific task of localizing circular shaped objects of known dimension - the pressure equalization openings on the underwater face of a hydro-dam. Despite the very good recognition performance of SVM classifiers, their success on such poor quality images is relatively modest. The new proposed architecture achieves the maximization of the correct classification rate in two steps. In the first step, a non-linear SVM classifier is trained on raw color pixel features extracted from regions of interest of approximately the object's size. In the classification phase, the underwater image to be analyzed is decomposed on such partially overlapping elementary regions of interest. The regions of interest are classified by the SVM, using as threshold for the decision function a real value selected to minimize the false rejection rate. Then, in the second step, a shape descriptor (the circularity) of the patterns classified by the SVM as objects of interest is computed. This shape descriptor of the positive patterns is used for their classification as objects of interest or not, through a simple threshold comparison. As a result of this classification, the false acceptance rate is minimized as well, thus refining the classification results