Exploiting inter-image similarity and ensemble of extreme learners for fixation prediction using deep features

Tutkimustuotos: LehtiartikkeliArticleScientificvertaisarvioitu

26 Sitaatiot (Scopus)

Abstrakti

This paper presents a novel fixation prediction and saliency modeling framework based on inter-image similarities and ensemble of Extreme Learning Machines (ELM). The proposed framework is inspired by two observations, (1) the contextual information of a scene along with low-level visual cues modulates attention, (2) the influence of scene memorability on eye movement patterns caused by the resemblance of a scene to a former visual experience. Motivated by such observations, we develop a framework that estimates the saliency of a given image using an ensemble of extreme learners, each trained on an image similar to the input image. That is, after retrieving a set of similar images for a given image, a saliency predictor is learnt from each of the images in the retrieved image set using an ELM, resulting in an ensemble. The saliency of the given image is then measured in terms of the mean of predicted saliency value by the ensemble’s members.
AlkuperäiskieliEnglanti
Sivut10-18
JulkaisuNeurocomputing
Vuosikerta244
DOI - pysyväislinkit
TilaJulkaistu - 2017
OKM-julkaisutyyppiA1 Julkaistu artikkeli, soviteltu

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  • Projektit

    • 1 Päättynyt

    Suomalainen laskennallisen päättelyn huippuyksikkö

    Xu, Y., Rezazadegan Tavakoli, H., Pesonen, H., Puranen, S., Rintanen, J., Kaski, S., Anwer, R., Parviainen, P., Soare, M., Weinzierl, A. & Vuollekoski, H.

    01/01/201528/02/2018

    Projekti: Academy of Finland: Other research funding

    Laitteet

    Science-IT

    Mikko Hakala (Manager)

    Perustieteiden korkeakoulu

    Laitteistot/tilat: Facility

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