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题名: DSIM: A DisSIMilarity-based image clutter metric for targeting performance
作者: Xu DJ(徐德江) ; Shi ZL(史泽林)
作者部门: 光电信息技术研究室
关键词: Image clutter metric ; signal-to-clutter ratio (SCR) ; dissimilarity metric (DSIM) ; human vision perceptual dissimilarity measure (VPDM) ; brain cognitive dissimilarity measure (BCDM)
刊名: IEEE Transactions on Image Processing
ISSN号: 1057-7149
出版日期: 2013
卷号: 22, 期号:10, 页码:4108-4122
收录类别: SCI ; EI
产权排序: 1
摘要: Previous image clutter metrics were proposed on the thought that clutter was just a perceptual effect, while we identify clutter as both perceptual and cognitive effects. Under this identification, we give a new definition of image clutter metric by analyzing the research results in the fields of visual psychology and psychophysics. According to the definition, we further put forward a DisSIMilarity (DSIM) based image clutter metric, which can also be taken as a kind of HVS-based signal-to-clutter ratio. The earlier image clutter metrics produced limited success in predicting targeting performance mainly since they did not consider brain cognitive characteristics. We develop a brain cognitive dissimilarity measure (BCDM) as a quantitative estimate of the selection weights which are allocated by brain attentional mechanism to affect visual selection processes. A human vision perceptual dissimilarity measure (VPDM), fully embodying vision perceptual properties, is first established between the target and clutter images, and then we utilize the BCDM between the two images as selection weights to pool the VPDM to be a clutter metric, which can be called DSIM metric. The metric is tested in Search-2 dataset provided by TNO Human Factors Research Institute of Netherlands. Error analysis and correlation tests demonstrate that the DSIM metric makes a more significant improvement than previously proposed metrics in predicting 62 observers' targeting performances including detection probability, false alarm probability and search time.
语种: 英语
WOS记录号: WOS:000324385200032
WOS标题词: Science & Technology ; Technology
类目[WOS]: Computer Science, Artificial Intelligence ; Engineering, Electrical & Electronic
关键词[WOS]: STRUCTURAL SIMILARITY ; DETECTION PROBABILITY ; QUALITY ASSESSMENT ; INFRARED IMAGES ; SEARCH ; INFORMATION ; SYSTEMS ; RECOGNITION ; ACQUISITION ; ATTENTION
研究领域[WOS]: Computer Science ; Engineering
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内容类型: 期刊论文
URI标识: http://ir.sia.cn/handle/173321/12526
Appears in Collections:光电信息技术研究室_期刊论文

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