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Abstract Games with a purpose (GWAP) and microtask crowdsourcing are considered two techniques of the human-computation. Using these techniques can help in improving the image retrieval systems (IMR) to be more accurate and helpful. It provides the IMR 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. This game is a type of games with a purpose. In the ESP game there has been a lot of work were 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 players’ answers for the same image that don’t match. In this work three algorithms were proposed to solve the problems of the ESP game. The first algorithm uses neglected data to generate new labels for the images. This algorithm first focuses on measuring the total number of labels generated by the proposed Recycle Unused Answers for Images algorithm (RUAI). The RUAI algorithm was evaluated by a quality of labels measure. This measure identifies the quality of the labels that were generated from the RUAI compared to the pre-qualified labels from the ESP game dataset. The results reveal that the proposed algorithm improved the results in compared to the ESP game in all cases. The second algorithm help in generating informative ESP game labels with no need to extra un-useful game rounds using one of the association rules mining algorithms (FP-growth). The results show that new informative labels can be generated automatically without any interference of extra game rounds or any human. The third algorithm was developed as a mobile game called MemoryLabel. It is a single player mobile game. It helps in labeling images and gives description for them. In addition, the game gives description for parts of the image not the whole image. In addition, the game is published on Google play market for android applications. In this trial, we first focused on measuring the total number of labels generated by our game and also the number of objects that have been labeled. The results reveal that the proposed game has promising results in describing images and objects. All three algorithms were evaluated at the Menoufia University by the demonstrators in computer science department. |