Unsupervised Salient Object Detection by Aggregating Multi-Level Cues Article Swipe

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YOU? · · 2018 · Open Access · · DOI: https://doi.org/10.1109/jphot.2018.2881271

In this paper, we present a novel method to detect salient object based on multi-level cues. First, a proposal processing scheme is developed by various object-level saliency cues to generate an initial saliency map. For the sake of more accurate object boundaries, a two-stage optimization mechanism is then proposed upon superpixel-level. Finally, the superpixel-level saliency map is further improved to construct the final saliency map by applying superpixel-to-pixel mapping. Extensive experimental results demonstrate that the proposed algorithm performs favorably against the state-of-art saliency detection methods in terms of different evaluation metrics on several benchmark datasets.

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Computer Science

Artificial Intelligence

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Mathematical Analysis

Geography

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Computer science Artificial intelligence Benchmark (surveying) Saliency map Pattern recognition (psychology) Salient Object (grammar) Object detection Pixel Computer vision Construct (python library) Scheme (mathematics) Mathematics Geodesy Programming language Mathematical analysis Geography

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Type: article
Language: en
Landing Page: https://doi.org/10.1109/jphot.2018.2881271
OA Status: gold
Cited By: 8
References: 36
Related Works: 10
OpenAlex ID: https://openalex.org/W2900745190

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DOI: https://doi.org/10.1109/jphot.2018.2881271

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Title: Unsupervised Salient Object Detection by Aggregating Multi-Level Cues

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Language: en

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Publication year: 2018

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Publication date: 2018-11-14

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Authors: Chenxing Xia, Hanling Zhang

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Landing page: https://doi.org/10.1109/jphot.2018.2881271

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Concepts: Computer science, Artificial intelligence, Benchmark (surveying), Saliency map, Pattern recognition (psychology), Salient, Object (grammar), Object detection, Pixel, Computer vision, Construct (python library), Scheme (mathematics), Mathematics, Geodesy, Programming language, Mathematical analysis, Geography

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Cited by: 8

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Citations by year (recent): 2025: 1, 2024: 1, 2023: 2, 2022: 2, 2021: 2

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References (count): 36

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Related works (count): 10

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abstract_inverted_index.benchmark	93
abstract_inverted_index.construct	60
abstract_inverted_index.datasets.	94
abstract_inverted_index.detection	83
abstract_inverted_index.developed	22
abstract_inverted_index.different	88
abstract_inverted_index.favorably	78
abstract_inverted_index.mechanism	45
abstract_inverted_index.two-stage	43
abstract_inverted_index.evaluation	89
abstract_inverted_index.processing	19
abstract_inverted_index.boundaries,	41
abstract_inverted_index.demonstrate	72
abstract_inverted_index.multi-level	14
abstract_inverted_index.experimental	70
abstract_inverted_index.object-level	25
abstract_inverted_index.optimization	44
abstract_inverted_index.state-of-art	81
abstract_inverted_index.superpixel-level	53
abstract_inverted_index.superpixel-level.	50
abstract_inverted_index.superpixel-to-pixel	67
cited_by_percentile_year.max	96
cited_by_percentile_year.min	90
countries_distinct_count	1
institutions_distinct_count	2
citation_normalized_percentile.value	0.57273708
citation_normalized_percentile.is_in_top_1_percent	False
citation_normalized_percentile.is_in_top_10_percent	False