Abstract
one of the human-computation techniques is games with a purpose (GWAP) and microtask crowdsourcing. These techniques can help in making the image retrieval (IR) be more accurate and helpful. It provides the IR system's database with a rich of information by adding more descriptions and annotations to images. One of the systems of human-computation is ESP Game. ESP Game is a type of games with a purpose. In the ESP game there has been a lot of work was proposed to solve many of the problems in it and make the most benefit of the game. One of these problems is that the ESP game neglects player's answers for the same image that don't match. This paper presents a new algorithm to use neglected data to generate new labels for the images. We deploy our algorithm at the University of Menoufia for evaluation. In this trial, we first focused on measuring the total number of labels generated by our Recycle Unused Answers For Images algorithm (RUAI). In our evaluation of the RUAI algorithm we present a new evaluation measure we called it quality of labels measure. This measure identifies the quality of the labels in compared to the pre-qualified labels. The results reveal that the proposed algorithm improved the results in compared to the ESP game in all cases.