Digital image processing (2nd ed.)
Digital image processing (2nd ed.)
Content-Based Image Retrieval at the End of the Early Years
IEEE Transactions on Pattern Analysis and Machine Intelligence
Efficient Graph-Based Image Segmentation
International Journal of Computer Vision
Distinctive Image Features from Scale-Invariant Keypoints
International Journal of Computer Vision
Lire: lucene image retrieval: an extensible java CBIR library
MM '08 Proceedings of the 16th ACM international conference on Multimedia
CEDD: color and edge directivity descriptor: a compact descriptor for image indexing and retrieval
ICVS'08 Proceedings of the 6th international conference on Computer vision systems
PCA-SIFT: a more distinctive representation for local image descriptors
CVPR'04 Proceedings of the 2004 IEEE computer society conference on Computer vision and pattern recognition
Semantically relevant image retrieval by combining image and linguistic analysis
ISVC'06 Proceedings of the Second international conference on Advances in Visual Computing - Volume Part II
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In this paper, we introduce a new content-based image retrieval (CBIR) system using SIFT combined with neural network and Graph-based segmentation technique. Like most CBIR systems, our system performs three main tasks: extracting image features, training data and retrieving images. In the task of image features extracting, we used our new mean SIFT features after segmenting image into objects using a graph-based method. We trained our data using neural network technique. Before the training step, we clustered our data using both supervised and unsupervised methods. Finally, we used individual object-based and multi object-based methods to retrieve images. In the experiments, we have tested our system to a database of 4848 images of 5 different categories with 400 other images as test queries. In addition, we compared our system to LIRE demo application using the same test set.