Saliency detection by aggregating complementary background template with optimization framework Article Swipe
Chenxing Xia
,
Hanling Zhang
,
Xiuju Gao
·
YOU?
·
· 2017
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.1706.04285
YOU?
·
· 2017
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.1706.04285
This paper proposes an unsupervised bottom-up saliency detection approach by aggregating complementary background template with refinement. Feature vectors are extracted from each superpixel to cover regional color, contrast and texture information. By using these features, a coarse detection for salient region is realized based on background template achieved by different combinations of boundary regions instead of only treating four boundaries as background. Then, by ranking the relevance of the image nodes with foreground cues extracted from the former saliency map, we obtain an improved result. Finally, smoothing operation is utilized to refine the foreground-based saliency map to improve the contrast between salient and non-salient regions until a close to binary saliency map is reached. Experimental results show that the proposed algorithm generates more accurate saliency maps and performs favorably against the state-off-the-art saliency detection methods on four publicly available datasets.
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Metadata
- Type
- preprint
- Language
- en
- Landing Page
- http://arxiv.org/abs/1706.04285
- https://arxiv.org/pdf/1706.04285
- OA Status
- green
- Cited By
- 1
- References
- 39
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W2626219075
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- OpenAlex ID
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https://openalex.org/W2626219075Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.48550/arxiv.1706.04285Digital Object Identifier
- Title
-
Saliency detection by aggregating complementary background template with optimization frameworkWork title
- Type
-
preprintOpenAlex work type
- Language
-
enPrimary language
- Publication year
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2017Year of publication
- Publication date
-
2017-06-14Full publication date if available
- Authors
-
Chenxing Xia, Hanling Zhang, Xiuju GaoList of authors in order
- Landing page
-
https://arxiv.org/abs/1706.04285Publisher landing page
- PDF URL
-
https://arxiv.org/pdf/1706.04285Direct link to full text PDF
- Open access
-
YesWhether a free full text is available
- OA status
-
greenOpen access status per OpenAlex
- OA URL
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https://arxiv.org/pdf/1706.04285Direct OA link when available
- Concepts
-
Artificial intelligence, Computer science, Salient, Contrast (vision), Pattern recognition (psychology), Smoothing, Feature (linguistics), Saliency map, Image (mathematics), Boundary (topology), Computer vision, Ranking (information retrieval), Mathematics, Linguistics, Philosophy, Mathematical analysisTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
1Total citation count in OpenAlex
- Citations by year (recent)
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2019: 1Per-year citation counts (last 5 years)
- References (count)
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39Number of works referenced by this work
- Related works (count)
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10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.extracted | 19, 74 |
| abstract_inverted_index.favorably | 128 |
| abstract_inverted_index.features, | 34 |
| abstract_inverted_index.generates | 121 |
| abstract_inverted_index.operation | 87 |
| abstract_inverted_index.relevance | 66 |
| abstract_inverted_index.smoothing | 86 |
| abstract_inverted_index.background | 12, 45 |
| abstract_inverted_index.boundaries | 59 |
| abstract_inverted_index.foreground | 72 |
| abstract_inverted_index.superpixel | 22 |
| abstract_inverted_index.aggregating | 10 |
| abstract_inverted_index.background. | 61 |
| abstract_inverted_index.non-salient | 103 |
| abstract_inverted_index.refinement. | 15 |
| abstract_inverted_index.Experimental | 114 |
| abstract_inverted_index.combinations | 50 |
| abstract_inverted_index.information. | 30 |
| abstract_inverted_index.unsupervised | 4 |
| abstract_inverted_index.complementary | 11 |
| abstract_inverted_index.foreground-based | 93 |
| abstract_inverted_index.state-off-the-art | 131 |
| cited_by_percentile_year | |
| countries_distinct_count | 0 |
| institutions_distinct_count | 3 |
| citation_normalized_percentile |