Visual saliency detection: a Kalman filter based approach

Sourya Roy; Pabitra Mitra

doi:10.48550/arxiv.1604.04825

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Visual saliency detection: a Kalman filter based approach

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Visual saliency detection: a Kalman filter based approach

Sourya Roy and Pabitra Mitra

ArXiv.org

Cornell University

04/16/2016

DOI: 10.48550/arxiv.1604.04825

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https://doi.org/10.48550/arXiv.1604.04825View

Preprint (Author's original)This preprint has not been evaluated by subject experts through peer review. Preprints may undergo extensive changes and/or become peer-reviewed journal articles. Open Access

Abstract

In this paper we propose a Kalman filter aided saliency detection model which is based on the conjecture that salient regions are considerably different from our "visual expectation" or they are "visually surprising" in nature. In this work, we have structured our model with an immediate objective to predict saliency in static images. However, the proposed model can be easily extended for space-time saliency prediction. Our approach was evaluated using two publicly available benchmark data sets and results have been compared with other existing saliency models. The results clearly illustrate the superior performance of the proposed model over other approaches.

Computer Science - Computer Vision and Pattern Recognition

Details

Title: Subtitle: Visual saliency detection: a Kalman filter based approach
Creators: Sourya Roy
Pabitra Mitra
Resource Type: Preprint
Publication Details: ArXiv.org
DOI: 10.48550/arxiv.1604.04825
ISSN: 2331-8422
Publisher: Cornell University
Language: English
Date posted: 04/16/2016
Academic Unit: Computer Science
Record Identifier: 9984447850902771

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