OntoAlbum: An Ontology Based Digital Photo Management System
ICIAR '08 Proceedings of the 5th international conference on Image Analysis and Recognition
Malicious content filtering based on semantic features
Proceedings of the 2nd International Conference on Interaction Sciences: Information Technology, Culture and Human
Exploit camera metadata for enhancing interesting region detection and photo retrieval
Multimedia Tools and Applications
Semantic annotation of personal video content using an image folksonomy
ICIP'09 Proceedings of the 16th IEEE international conference on Image processing
Mr.KNN: soft relevance for multi-label classification
CIKM '10 Proceedings of the 19th ACM international conference on Information and knowledge management
PCM'10 Proceedings of the 11th Pacific Rim conference on Advances in multimedia information processing: Part I
A simple approach to incorporate label dependency in multi-label classification
MICAI'10 Proceedings of the 9th Mexican international conference on Artificial intelligence conference on Advances in soft computing: Part II
Voting based learning classifier system for multi-label classification
Proceedings of the 13th annual conference companion on Genetic and evolutionary computation
Salient region detection using discriminative feature selection
ACIVS'11 Proceedings of the 13th international conference on Advanced concepts for intelligent vision systems
Incorporating label dependency into the binary relevance framework for multi-label classification
Expert Systems with Applications: An International Journal
Semantic concept detection for user-generated video content using a refined image folksonomy
MMM'10 Proceedings of the 16th international conference on Advances in Multimedia Modeling
Photo retrieval combining ontology with visual information
Proceedings of the 2012 ACM Research in Applied Computation Symposium
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A semantic categorization method for generic home photo is proposed. The main contribution of this paper is to exploit a two-layered classification model incorporating camera metadata with low-level features for multilabel detection. The two-layered support vector machine (SVM) classifiers operate to detect local and global photo semantics in a feed-forward way. The first layer aims to predict likelihood of predefined local photo semantics based on camera metadata and regional low-level visual features. In the second layer, one or more global photo semantics is detected based on the likelihood. To construct classifiers producing a posterior probability, we use a parametric model to fit the output of SVM classifiers to posterior probability. A concept merging process based on a set of semantic-confidence maps is also presented to cope with selecting more likelihood photo semantics on spatially overlapping local regions. Experiment was performed with 3086 photos that come from MPEG-7 visual core experiment two official databases. Results showed that the proposed method would much better capture multiple semantic meanings of home photos, compared to other similar technologies