User Information Needs

Abstract

Computer vision technology has been considered in marine ecology research as a innovative, promising data collection method. It contrasts with traditional practices in the information that is collected, and its inherent errors and biases. Ecology research is based on the analysis of biological characteristics (e.g., species, size, age, distribution, density, behaviors), while computer vision focuses on visual characteristics that are not necessarily related to biological concepts (e.g., contours, contrasts, color histograms, background model). It is challenging for ecologists to assess the scientific validity of surveys performed on the basis of image analysis. User information needs may not be fully addressed by image features, or may not be reliable enough. We gathered user requirements for supporting ecology research based on computer vision technologies, and identified those we can address within the Fish4Knowledge project. We particularly investigated the uncertainty inherent to computer vision technology, and the means to support users in considering uncertainty when interpreting information on fish populations. We introduce potential biases and uncertainty factors that can impact the scientific validity of interpretations drawn from computer vision results. We conclude by introducing potential approaches for providing users with evaluations of the uncertainties introduced at each information processing step.