A Model of Saliency-Based Visual Attention for Rapid Scene Analysis
IEEE Transactions on Pattern Analysis and Machine Intelligence
MISSL: multiple-instance semi-supervised learning
ICML '06 Proceedings of the 23rd international conference on Machine learning
Proceedings of the 18th international conference on World wide web
Inferring semantic concepts from community-contributed images and noisy tags
MM '09 Proceedings of the 17th ACM international conference on Multimedia
NUS-WIDE: a real-world web image database from National University of Singapore
Proceedings of the ACM International Conference on Image and Video Retrieval
Learning social tag relevance by neighbor voting
IEEE Transactions on Multimedia
Proceedings of the ACM International Conference on Image and Video Retrieval
Image annotation by kNN-sparse graph-based label propagation over noisily tagged web images
ACM Transactions on Intelligent Systems and Technology (TIST)
Adaptive all-season image tag ranking by saliency-driven image pre-classification
Journal of Visual Communication and Image Representation
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Social image tag ranking has emerged as an important research topic recently due to its potential application on web image search. This paper presents an adaptive all-season tag ranking algorithm which can handle the images with and without distinct object(s) using different tag ranking strategies. Firstly, based on saliency map derived from the visual attention model, a linear SVM is trained to pre-classify an image as attentive or non-attentive category by using the gray histogram descriptor on the corresponding saliency map. Then, an image with distinct object is processed by an attention-driven tag saliency ranking algorithm emphasizing distinct object. On the other hand, an image without distinct object is processed by the tag relevance ranking algorithm via the sparse representation based neighbor-voting strategy. Such adaptive ranking strategy can be regarded as taking full advantage of existing tag ranking paradigms. Experiments conducted on well-known image data sets demonstrate the effectiveness and efficiency of the proposed framework.