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231224s2016 xx |||||o 00| ||eng c |
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|a 10.1109/TIP.2016.2593653
|2 doi
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|a pubmed24n0875.xml
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|a DE-627
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|e rakwb
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| 041 |
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|a eng
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| 100 |
1 |
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|a Xiaopeng Yang
|e verfasserin
|4 aut
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| 245 |
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|a Web Image Search Re-Ranking With Click-Based Similarity and Typicality
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|c 2016
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|a Text
|b txt
|2 rdacontent
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|a ƒaComputermedien
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|2 rdamedia
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|a ƒa Online-Ressource
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| 500 |
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|a Date Revised 20.11.2019
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|a published: Print-Electronic
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|a Citation Status PubMed-not-MEDLINE
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| 520 |
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|a In image search re-ranking, besides the well-known semantic gap, intent gap, which is the gap between the representation of users' query/demand and the real intent of the users, is becoming a major problem restricting the development of image retrieval. To reduce human effects, in this paper, we use image click-through data, which can be viewed as the implicit feedback from users, to help overcome the intention gap, and further improve the image search performance. Generally, the hypothesis-visually similar images should be close in a ranking list-and the strategy-images with higher relevance should be ranked higher than others-are widely accepted. To obtain satisfying search results, thus, image similarity and the level of relevance typicality are determinate factors correspondingly. However, when measuring image similarity and typicality, conventional re-ranking approaches only consider visual information and initial ranks of images, while overlooking the influence of click-through data. This paper presents a novel re-ranking approach, named spectral clustering re-ranking with click-based similarity and typicality. First, to learn an appropriate similarity measurement, we propose click-based multi-feature similarity learning algorithm, which conducts metric learning based on click-based triplets selection, and integrates multiple features into a unified similarity space via multiple kernel learning. Then, based on the learnt click-based image similarity measure, we conduct spectral clustering to group visually and semantically similar images into same clusters, and get the final re-rank list by calculating click-based clusters typicality and within-clusters click-based image typicality in descending order. Our experiments conducted on two real-world query-image data sets with diverse representative queries show that our proposed re-ranking approach can significantly improve initial search results, and outperform several existing re-ranking approaches
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| 650 |
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|a Journal Article
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| 700 |
1 |
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|a Tao Mei
|e verfasserin
|4 aut
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| 700 |
1 |
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|a Yongdong Zhang
|e verfasserin
|4 aut
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| 700 |
1 |
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|a Jie Liu
|e verfasserin
|4 aut
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| 700 |
1 |
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|a Satoh, Shin'ichi
|e verfasserin
|4 aut
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| 773 |
0 |
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|i Enthalten in
|t IEEE transactions on image processing : a publication of the IEEE Signal Processing Society
|d 1992
|g 25(2016), 10 vom: 02. Okt., Seite 4617-4630
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|g volume:25
|g year:2016
|g number:10
|g day:02
|g month:10
|g pages:4617-4630
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| 856 |
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|u http://dx.doi.org/10.1109/TIP.2016.2593653
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