Content-based image retrieval: approaches and trends of the new age
Proceedings of the 7th ACM SIGMM international workshop on Multimedia information retrieval
Adaptive image retrieval using a Graph model for semantic feature integration
MIR '06 Proceedings of the 8th ACM international workshop on Multimedia information retrieval
Naked image detection based on adaptive and extensible skin color model
Pattern Recognition
Deriving semantics for image clustering from accumulated user feedbacks
Proceedings of the 15th international conference on Multimedia
Image retrieval: Ideas, influences, and trends of the new age
ACM Computing Surveys (CSUR)
Automatic medical image annotation and retrieval
Neurocomputing
Content-based image retrieval using visually significant point features
Fuzzy Sets and Systems
A Bayesian Approach to Hybrid Image Retrieval
PReMI '09 Proceedings of the 3rd International Conference on Pattern Recognition and Machine Intelligence
A fuzzy combined learning approach to content-based image retrieval
ICME'09 Proceedings of the 2009 IEEE international conference on Multimedia and Expo
Relevance feedback and latent semantic index based cultural relic image retrieval
Edutainment'07 Proceedings of the 2nd international conference on Technologies for e-learning and digital entertainment
Image retrieval using fuzzy relevance feedback and validation with MPEG-7 content descriptors
PReMI'07 Proceedings of the 2nd international conference on Pattern recognition and machine intelligence
A relevance feedback framework for image retrieval based on ant colony algorithm
ISVC'11 Proceedings of the 7th international conference on Advances in visual computing - Volume Part II
Region-based semantic similarity propagation for image retrieval
PCM'06 Proceedings of the 7th Pacific Rim conference on Advances in Multimedia Information Processing
Visual query processing for efficient image retrieval using a SOM-based filter-refinement scheme
Information Sciences: an International Journal
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Most current content-based image retrieval systems are still incapable of providing users with their desired results. The major difficulty lies in the gap between low-level image features and high-level image semantics. To address the problem, this study reports a framework for effective image retrieval by employing a novel idea of memory learning. It forms a knowledge memory model to store the semantic information by simply accumulating user-provided interactions. A learning strategy is then applied to predict the semantic relationships among images according to the memorized knowledge. Image queries are finally performed based on a seamless combination of low-level features and learned semantics. One important advantage of our framework is its ability to efficiently annotate images and also propagate the keyword annotation from the labeled images to unlabeled images. The presented algorithm has been integrated into a practical image retrieval system. Experiments on a collection of 10 000 general-purpose images demonstrate the effectiveness of the proposed framework.