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A multiple instance learning based framework for semantic image segmentation
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DOI：10.1007/s11042-009-0347-z.png)
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Abstract
En 中文
Most image segmentation algorithms extract regions satisfying visual uniformity criteria. Unfortunately, because of the semantic gap between low-level features and high-level semantics, such regions usually do not correspond to meaningful parts. This has motivated researchers to develop methods that, by introducing high-level knowledge into the segmentation process, can break through the performance ceiling imposed by the semantic gap. The main disadvantage of those methods is their lack of flexibility due to the assumption that such knowledge is provided in advance. In content-based image retrieval (CBIR), relevance feedback (RF) learning has been successfully applied as a technique aimed at reducing the semantic gap. Inspired by this, we present a RF-based CBIR framework that uses multiple instance learning to perform a semantically-guided context adaptation of segmentation parameters. A partial instantiation of this framework that uses mean shift-based segmentation is presented. Experiments show the effectiveness and flexibility of the proposed framework on real images.
Keywords:
Image segmentation
Multiple instance learning
Content-based image retrieval
Relevance feedback
Mean shift
Semantic gap
Adaptive segmentation
Segmentation parameters
Diverse density
Clustering
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