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

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


Toronto dataset modeling


Toronto dataset modeling


Toronto dataset modeling


Toronto dataset modeling


Toronto dataset modeling


Visual saliency
Visual Saliency modeling

  • 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
Benchmarks modeling

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

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

  • Yet another benchmark!!!?


Dataset challenge

Toronto modeling

MIT

Le Meur

Dataset Challenge

  • Dataset bias :

  • Center-Bias (CB),

    • Border effect

  • Metrics are affected by these phenomena.


  • Tricking the metric
    Tricking the metric modeling

    Solution ?

    • sAUC

    • Best smoothing factor

    • More than one metric


    The benchmark fixation prediction
    The Benchmark modeling Fixation Prediction


    The feature crises

    Features modeling

    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 modeling Image categories and affective data


    The benchmark image categories and affective data1
    The Benchmark modeling Image categories and affective data

    vs 0.64(non-emotional)


    The benchmark predicting scanpath

    aAdDbBcCaA modeling

    aAcCaA

    aAcCbBcCaAaA

    ….

    The Benchmarkpredicting scanpath

    aAbBcCaA

    aA

    dD

    bB

    cC

    bBbBcC

    matching score


    The benchmark predicting scanpath scores
    The Benchmark modelingpredicting scanpath (scores)



    Lessons learned
    Lessons learned modeling

    • 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 ?? modeling


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