Visual Learning of Weight from Shape using Support Vector Machines

We investigate the automatic estimation of fish weight from sets of morphometric measurements. Our solution combines a vision system with a robust regression method, the Support Vector Machine (SVM). Measurements are taken automatically from two binarised views of each fish in a training sample, then fed to a quadratic SVM along with approximate weight estimates. The SVM learns the law linking weight to shape directly (without computing volume) and compensates for several inaccuracies in the training measurements. We suggest a methodology identifying optimal shape measurements for the task, and report results obtained with a sample of 99 trouts between 300 and 600g, showing good accuracy and reliability, and better performance with respect to length-weight relations adopted commonly in fisheries science.