Semantic Adaptation of Neural Network Classifiers in Image Segmentation
ICANN '08 Proceedings of the 18th international conference on Artificial Neural Networks, Part I
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Knowledge-Based Systems
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Knowledge-Based Systems
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Reasoning with very expressive fuzzy description logics
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IEEE Transactions on Multimedia - Special issue on integration of context and content
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DSP'09 Proceedings of the 16th international conference on Digital Signal Processing
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ACM Transactions on Multimedia Computing, Communications, and Applications (TOMCCAP)
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WILF'11 Proceedings of the 9th international conference on Fuzzy logic and applications
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KES'11 Proceedings of the 15th international conference on Knowledge-based and intelligent information and engineering systems - Volume Part II
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ICIAR'12 Proceedings of the 9th international conference on Image Analysis and Recognition - Volume Part I
Multifeature analysis and semantic context learning for image classification
ACM Transactions on Multimedia Computing, Communications, and Applications (TOMCCAP)
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In this paper, we present a framework for simultaneous image segmentation and object labeling leading to automatic image annotation. Focusing on semantic analysis of images, it contributes to knowledge-assisted multimedia analysis and bridging the gap between semantics and low level visual features. The proposed framework operates at semantic level using possible semantic labels, formally represented as fuzzy sets, to make decisions on handling image regions instead of visual features used traditionally. In order to stress its independence of a specific image segmentation approach we have modified two well known region growing algorithms, i.e., watershed and recursive shortest spanning tree, and compared them to their traditional counterparts. Additionally, a visual context representation and analysis approach is presented, blending global knowledge in interpreting each object locally. Contextual information is based on a novel semantic processing methodology, employing fuzzy algebra and ontological taxonomic knowledge representation. In this process, utilization of contextual knowledge re-adjusts labeling results of semantic region growing, by means of fine-tuning membership degrees of detected concepts. The performance of the overall methodology is evaluated on a real-life still image dataset from two popular domains