Do humans fixate on interest points?

Akshat Dave*, Rachit Dubey, Bernard Ghanem

*Corresponding author for this work

    Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

    8 Scopus citations

    Abstract

    Interest point detectors (e.g. SIFT, SURF, and MSER) have been successfully applied to numerous applications in high level computer vision tasks such as object detection, and image classification. Despite their popularity, the perceptual relevance of these detectors has not been thoroughly studied. Here, perceptual relevance is meant to define the correlation between these point detectors and free-viewing human fixations on images. In this work, we provide empirical evidence to shed light on the fundamental question: 'Do humans fixate on interest points in images?'. We believe that insights into this question may play a role in improving the performance of vision systems that utilize these interest point detectors. We conduct an extensive quantitative comparison between the spatial distributions of human fixations and automatically detected interest points on a recently released dataset of 1003 images. This comparison is done at both the global (image) level as well as the local (region) level. Our experimental results show that there exists a weak correlation between the spatial distributions of human fixation and interest points.

    Original languageEnglish (US)
    Title of host publicationICPR 2012 - 21st International Conference on Pattern Recognition
    Pages2784-2787
    Number of pages4
    StatePublished - 2012
    Event21st International Conference on Pattern Recognition, ICPR 2012 - Tsukuba, Japan
    Duration: Nov 11 2012Nov 15 2012

    Publication series

    NameProceedings - International Conference on Pattern Recognition
    ISSN (Print)1051-4651

    Other

    Other21st International Conference on Pattern Recognition, ICPR 2012
    Country/TerritoryJapan
    CityTsukuba
    Period11/11/1211/15/12

    ASJC Scopus subject areas

    • Computer Vision and Pattern Recognition

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