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Saliency Modeling from Image Histograms
Shijian Lu and Joo-Hwee Lim
IPAL (UMI CNRS 2955), Institute for Infocomm Research, A*STAR, 1 Fusionopolis Way, #21-01 Connexis, Singapore, 138632slu@i2r.a-star.edu.sg
joohwee@i2r.a-star.edu.sg
Abstract. We proposed a computational visual saliency modeling technique. The proposed technique makes use of a color co-occurrence histogram (CCH) that captures not only “how many” but also “where and how” image pixels are composed into a visually perceivable image. Hence the CCH encodes image saliency information that is usually perceived as the discontinuity between an image region or object and its surrounding. The proposed technique has a number of distinctive characteristics: It is fast, discriminative, tolerant to image scale variation, and involves minimal parameter tuning. Experiments over benchmarking datasets show that it predicts fixational eye tracking points accurately and a superior AUC of 71.25 is obtained.
Keywords: Attention, saliency modeling, co-occurrence histogram
LNCS 7578, p. 321 ff.
lncs@springer.com
© Springer-Verlag Berlin Heidelberg 2012