A Unified Model for Salient Object Detection

Abdullah J. Alzahrani; Hina Afridi

doi:10.20944/preprints201904.0244.v1

Preprint Article Version 1 Preserved in Portico This version is not peer-reviewed

Version 1 : Received: 20 April 2019 / Approved: 22 April 2019 / Online: 22 April 2019 (11:40:11 CEST)

We propose a novel method for salient object detection in different images. Our method integrates spatial features for efficient and robust representation to capture meaningful information about the salient objects. We then train a conditional random field (CRF) using the integrated features. The trained CRF model is then used to detect salient objects during the online testing stage. We perform experiments on two standard datasets and compare the performance of our method with different reference methods. Our experiments show that our method outperforms the compared methods in terms of precision, recall, and F-Measure.

salient object; local binary pattern; histogram features; conditional random field

Computer Science and Mathematics, Computer Science

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A Unified Model for Salient Object Detection

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