Reduced data set for multi-target recognition using compressed sensing frame

Oceanographic plankton classification for many images is time consuming, especially for low-contrast images obtained when the water is dirty and opaque due to impurities. A novel, overcomplete dictionary algorithm is studied by analyzing the sparse characteristics of the image matrix using pixel values. The features in hyperspace are mapped onto a specifically designed vector space. Thus, mathematically, the algorithm exhibits faster calculating convergence and has a strong expressivity for the selective signal in the vector space with a lower signal loss rate. The clustering method based on the dictionary can classify planktons for species counting, which can enable high-speed, multi-object recognition of planktons in turbid water. The experimental results demonstrate that if less data (up to 60%) is processed for each image, a recall rate and accuracy greater than 75% and a structural similarity for the reconstructed image greater than 0.9 can be achieved.