Learning the Relative Importance of Objects from Tagged Images for Retrieval and Cross-Modal Search

We introduce an approach to image retrieval and auto-tagging that leverages the implicit information about object importance conveyed by the list of keyword tags a person supplies for an image. We propose an unsupervised learning procedure based on Kernel Canonical Correlation Analysis that discover...

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Vydáno v:	International journal of computer vision Ročník 100; číslo 2; s. 134 - 153
Hlavní autoři:	Hwang, Sung Ju, Grauman, Kristen
Médium:	Journal Article
Jazyk:	angličtina
Vydáno:	Boston Springer US 01.11.2012 Springer Springer Nature B.V
Témata:	Analysis Artificial Intelligence Computer Imaging Computer Science Correlation analysis Experiments Human subjects Image Processing and Computer Vision Image retrieval Information management Information retrieval International Keywords Learning Learning strategies Pattern Recognition Pattern Recognition and Graphics Perceptions Queries Semantics Studies Vision Vision systems United Kingdom > UK Image search Cross-modal retrieval Image tags Image retrieval Multi-modal retrieval Kernelized canonical correlation analysis Object recognition Auto annotation
ISSN:	0920-5691, 1573-1405
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