Large-Scale Concept Ontology for Multimedia
IEEE MultiMedia
ML-KNN: A lazy learning approach to multi-label learning
Pattern Recognition
An Empirical Study of Lazy Multilabel Classification Algorithms
SETN '08 Proceedings of the 5th Hellenic conference on Artificial Intelligence: Theories, Models and Applications
Using visual context and region semantics for high-level concept detection
IEEE Transactions on Multimedia - Special issue on integration of context and content
Learning to Detect a Salient Object
IEEE Transactions on Pattern Analysis and Machine Intelligence
Random k-Labelsets for Multilabel Classification
IEEE Transactions on Knowledge and Data Engineering
Effective Semantic Annotation by Image-to-Concept Distribution Model
IEEE Transactions on Multimedia
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Image automatic annotation is a promising and essential step for semantic image retrieval, and it's still a challenge because of the open problem of semantic gap. Recently, most of image annotation approaches paid more attention to detect single label for an image, but in fact they are multi-label learning problems. In this paper, we propose a new multi-model method for image multi-label annotation, which includes two different models for foreground and background semantic detection in terms of their distinct characters of semantic and visual features respectively, and a semantic correlation analysis model for refining the annotation results. A new visual saliency analysis algorithm based on multi-feature is proposed to obtaining the salient object, and multiple Nyström-approximating kernel discriminant analysis is used to acquire foreground semantic concept. Region semantic analysis is proposed to get annotation words of background, and semantic correlation matrix constructed by Latent Semantic Analysis is used to remove the unreliable labels. Experimental results show that our multi-model image labeling method could achieve promising performance for multi-labeling, and outperform previous methods on benchmark datasets.