Using Dual Cascading Learning Frameworks for Image Indexing

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Using Dual Cascading Learning Frameworks for Image Indexing
To bridge the semantic gap in content-based image retrieval, detecting meaningful visual entities (e.g. faces, sky, foliage, buildings etc) in image content and classifying images into semantic categories based on trained pattern classifiers have become active research trends. In this paper, we present dual cascading learning frameworks that extract and combine intraimage and inter-class semantics for image indexing and retrieval. In the supervised learning version, support vector detectors are trained on semantic support regions without image segmentation. The reconciled and aggregated detection-based indexes then serve as input for support vector learning of image classifiers to generate class-relative image indexes. During retrieval, similarities based on both indexes are combined to rank images. In the unsupervised learning approach, image classifiers are first trained on local image blocks from a small number of labeled images. Then local semantic patterns are discovered from...
Joo-Hwee Lim, Jesse S. Jin
Added 01 Nov 2010
Updated 01 Nov 2010
Type Conference
Year 2003
Where VIP
Authors Joo-Hwee Lim, Jesse S. Jin
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