DocumentCode :
3561191
Title :
Purposive Hidden-Object-Game: Embedding Human Computation in Popular Game
Author :
Feng, Jiashi ; Ni, Yuzhao ; Dong, Jian ; Wang, Zilei ; Yan, Shuicheng
Author_Institution :
Dept. of Electr. & Comput. Eng., Nat. Univ. of Singapore, Singapore, Singapore
Volume :
14
Issue :
5
fYear :
2012
Firstpage :
1496
Lastpage :
1507
Abstract :
Having sufficient training images with fully annotated object locations is undoubtedly critical for modern learning-based image annotation, retrieval, and object detection methods. Typically, collecting such annotations for large-scale datasets is notoriously tedious because the process involves amount of manual cropping and hand labeling operations. In this work, following the principle of games with a purpose (GWAP), we design a so-called purposive hidden-object-game (P-HOG), which imperceptibly embeds localizing objects into enjoyable playing game process and thus attracts many people to make voluntary contribution to annotating images. In particular, besides preserving the interestingness as popular HOG games, P-HOG is able to automatically generate satisfactory game images (i.e., “hide” certain items into target images) by integrating several semantic and visual processing techniques. P-HOG is also built in an effective mechanism to prevent the players from cheating. The mechanism inherits the merit of Recaptcha and identifies potential cheating behavior based on the annotation accuracy of some known items. Moreover, P-HOG will filter noisy annotations effectively based on a weighted majority method and improve the accuracy of the raw annotations from the players. Most importantly, players only play P-HOG for entertainment purpose and they are unaware of the background data collection procedure. The collected data are used towards constructing a large database, which may benefit general learning-based algorithms for multimedia tasks. To the best of our knowledge, this is the first work dedicated to such a specific and important task under the GWAP framework. We conduct a pilot study of the game prototype and the comprehensive experiments show that the P-HOG appeals to general players, and is effective for collecting massive object locations with satisfactory accuracy, which further boosts the algorithmic performances for both tag refinement and- image annotation tasks.
Keywords :
computer games; embedded systems; image retrieval; learning (artificial intelligence); multimedia computing; object detection; GWAP framework; HOG games; P-HOG; data collection procedure; embedding human computation; filter noisy annotations; fully annotated object locations; game images; game process; game prototype; games with a purpose; hand labeling operations; image annotation tasks; image retrieval; large-scale datasets; learning-based algorithm; learning-based image annotation; multimedia tasks; object detection; purposive hidden-object-game; recaptcha; tag refinement; training images; visual processing techniques; weighted majority method; Accuracy; Games; Humans; Labeling; Multimedia communication; Noise measurement; Semantics; Games with a purpose (GWAP); human computing; image processing; multimedia computing;
fLanguage :
English
Journal_Title :
Multimedia, IEEE Transactions on
Publisher :
ieee
Conference_Location :
5/10/2012 12:00:00 AM
ISSN :
1520-9210
Type :
jour
DOI :
10.1109/TMM.2012.2198801
Filename :
6198357
Link To Document :
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