A unified framework for semantics and feature based relevance feedback in image retrieval systems
MULTIMEDIA '00 Proceedings of the eighth ACM international conference on Multimedia
Content-Based Image Retrieval at the End of the Early Years
IEEE Transactions on Pattern Analysis and Machine Intelligence
Performance evaluation in content-based image retrieval: overview and proposals
Pattern Recognition Letters - Special issue on image/video indexing and retrieval
Unifying Keywords and Visual Contents in Image Retrieval
IEEE MultiMedia
Vidya: an experiential annotation system
ETP '03 Proceedings of the 2003 ACM SIGMM workshop on Experiential telepresence
A hybrid relevance-feedback approach to text retrieval
ECIR'03 Proceedings of the 25th European conference on IR research
Text-image interaction for image retrieval and semi-automatic indexing
IRSG'98 Proceedings of the 20th Annual BCS-IRSG conference on Information Retrieval Research
Image classification for content-based indexing
IEEE Transactions on Image Processing
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Content-based image retrieval and text-based image retrieval are two Fundamental approaches in the field of image retrieval. Recently, the researchers use the combining approaches and semiautomatic image retrieval, using the user interaction in the retrieval cycle. In this paper, an image retrieval approach is introduced that provides the retrieval process semi-automatically using the relevance feedbacks of the user and the novel similarity metrics for related high level semantic labels to the images. The proposed approach can reply different requests in the image retrieval domain based on a hierarchical semantic network and doing a new kind of learning process by the feedbacks given by user. According to experiments, our proposed approach concludes considerable accuracy for retrieval results.