2 June 2000 Ground truth for training and evaluation of automatic main subject detection
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Abstract
A consumer photograph, or snapshot, is a medium for conveying to a viewer, one's interest in one or more main subjects. A methodology is presented for collecting ground truth data useful for training and evaluating algorithms designed to automatically detect the main subject of a consumer photograph. For a database of 100 images, 16 observers provided polygonal approximations to the image areas that comprise the main subject. Results from all observer are combined to form a truth image that is considered the ideal result of a main subject detector and is analyzed to determine features for main subject detection (MSD). The collected ground truth shows substantial agreement among third-party observers. It also supports conventional wisdom regarding the likely locations of main subjects and the value of 'people' detection as a cue for main subject detection. Training data is created from the truth images for an MSD framework involving image segmentation, feature detection, and probabilistic reasoning. A proposed method for generating region-based training data can be used to retrain a reasoning engine as segmentation algorithms improve, without further observer involvement. Although the subject matter for consumer photographs ranges from sweeping landscapes to close portraits, identification of the main subject is a meaningful task.
© (2000) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Stephen P. Etz and Jiebo Luo "Ground truth for training and evaluation of automatic main subject detection", Proc. SPIE 3959, Human Vision and Electronic Imaging V, (2 June 2000); doi: 10.1117/12.387181; https://doi.org/10.1117/12.387181
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