prl112_241.pdf 1.44 MB
西山 正志 Graduate School of Engineering, Tottori University / Cross-informatics Research Center, Tottori University 研究者総覧 KAKEN
Matsumoto, Riku Graduate School of Engineering, Tottori University
We discuss how to reveal and use the gaze locations of observers who view pedestrian images for personal attribute classification. Observers look at informative regions when attempting to classify the attributes of pedestrians in images. Thus, we hypothesize that the regions in which observers’ gaze locations are clustered will contain discriminative features for the classifiers of personal attributes. Our method acquires the distribution of gaze locations from several observers while they perform the task of manually classifying each personal attribute. We term this distribution a task-oriented gaze map. To extract discriminative features, we assign large weights to the region with a cluster of gaze locations in the task-oriented gaze map. In our experiments, observers mainly looked at different regions of body parts when classifying each personal attribute. Furthermore, our experiments show that the gaze-based feature extraction method significantly improved the performance of personal attribute classification when combined with a convolutional neural network or metric learning technique.
PATTERN RECOGNITION LETTERS
(C) 2018. This manuscript version is made available under the CC-BY-NC-ND 4.0 license https://creativecommons.org/licenses/by-nc-nd/4.0/
Nishiyama Masashi, Matsumoto Riku, Yoshimura Hiroki, et al. Extracting discriminative features using task-oriented gaze maps measured from observers for personal attribute classification. PATTERN RECOGNITION LETTERS. 2018. 112. 241-248. doi:10.1016/j.patrec.2018.08.001
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