Object Recognition as Machine Translation: Learning a Lexicon for a Fixed Image Vocabulary
ECCV '02 Proceedings of the 7th European Conference on Computer Vision-Part IV
Automatic image annotation and retrieval using cross-media relevance models
Proceedings of the 26th annual international ACM SIGIR conference on Research and development in informaion retrieval
A survey of content-based image retrieval with high-level semantics
Pattern Recognition
IGroup: presenting web image search results in semantic clusters
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
A Shape Ontology Framework for Bird Classification
DICTA '07 Proceedings of the 9th Biennial Conference of the Australian Pattern Recognition Society on Digital Image Computing Techniques and Applications
Real-Time Computerized Annotation of Pictures
IEEE Transactions on Pattern Analysis and Machine Intelligence
Learning in region-based image retrieval
CIVR'03 Proceedings of the 2nd international conference on Image and video retrieval
Studying aesthetics in photographic images using a computational approach
ECCV'06 Proceedings of the 9th European conference on Computer Vision - Volume Part III
Training artificial neural networks using APPM
International Journal of Wireless and Mobile Computing
A human-oriented image retrieval system using interactive genetic algorithm
IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans
A new stochastic algorithm to direct orbits of chaotic systems
International Journal of Computer Applications in Technology
Contrast enhancement of fog and haze stereo images based on mobile computing
International Journal of Wireless and Mobile Computing
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Nowadays, more and more digital images are available. How to find a required image quickly for a user has become harder and harder. Acquiring semantic information of images and realising automatic image annotation is an effective technology to improve the performance of image retrieval. This paper presents a method of sentiment annotation of natural images based on fuzzy theory. The method describes image emotional level by computing fuzzy membership degree, uses BP neural network to implement it and solves semantic ambiguity on automatic image annotation. Using 967 natural images downloaded by Baidu photo channel to train and test, experiments achieved good effect compared with manual annotation results. The proposed method can lay a good foundation for automatic semantic annotation of more types of images.