Analysis of scores datasets and models in visual saliency modeling
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Analysis of scores, datasets, and models in visual saliency modeling. Ali Borji, Hamed R. Tavakoli, Dicky N. Sihite, and Laurent Itti,. Toronto dataset. Toronto dataset. Toronto dataset. Toronto dataset. Toronto dataset. Visual Saliency. Why important? Current status

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Analysis of scores, datasets, and models in visual saliency modeling

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Analysis of scores, datasets, and models in visual saliency modeling

  • Ali Borji, Hamed R. Tavakoli, Dicky N. Sihite, and Laurent Itti,


Toronto dataset


Toronto dataset


Toronto dataset


Toronto dataset


Toronto dataset


Visual Saliency

  • Why important?

  • Current status

  • Methods: numerous / 8 categories (Borji and Itti, PAMI, 2012)

  • Databases:

  • Measures:

    • scan-path analysis

    • correlation based measures

    • ROC analysis

How good my method works?


Benchmarks

  • Judd et al. http://people.csail.mit.edu/tjudd/SaliencyBenchmark/

  • Borji and Itti https://sites.google.com/site/saliencyevaluation/

  • Yet another benchmark!!!?


Toronto

MIT

Le Meur

Dataset Challenge

  • Dataset bias :

  • Center-Bias (CB),

    • Border effect

  • Metrics are affected by these phenomena.


  • Tricking the metric

    Solution ?

    • sAUC

    • Best smoothing factor

    • More than one metric


    The Benchmark Fixation Prediction


    Features

    Low level

    High level

    people

    car

    intensity

    color

    symmetry

    orientation

    signs

    depth

    text

    size

    The Feature Crises

    Does it capture any semantic scene property or affective stimuli?

    Challenge of performance on stimulus categories

    &

    affective stimuli


    The Benchmark Image categories and affective data


    The Benchmark Image categories and affective data

    vs 0.64(non-emotional)


    aAdDbBcCaA

    aAcCaA

    aAcCbBcCaAaA

    ….

    The Benchmarkpredicting scanpath

    aAbBcCaA

    aA

    dD

    bB

    cC

    bBbBcC

    matching score


    The Benchmarkpredicting scanpath (scores)


    Category Decoding


    Lessons learned

    • We recommend using shuffled AUC score for model evaluation.

    • Stimuli affects the performance .

    • Combination of saliency and eye movement statistics can be used in category recognition.

    • There seems the gap between models and IO is small (though statistically significant). It somehow alerts the need for new dataset.

    • The challenge of task decoding using eye statistics is open yet.

    • Saliency evaluation scores can still be introduced


    Questions ??


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