Distinctive Image Features from Scale-Invariant Keypoints
International Journal of Computer Vision
International Journal of Computer Vision
Endomicroscopic image retrieval and classification using invariant visual features
ISBI'09 Proceedings of the Sixth IEEE international conference on Symposium on Biomedical Imaging: From Nano to Macro
MICCAI'10 Proceedings of the 13th international conference on Medical image computing and computer-assisted intervention: Part II
IPMI'11 Proceedings of the 22nd international conference on Information processing in medical imaging
Retrieval evaluation and distance learning from perceived similarity between endomicroscopy videos
MICCAI'11 Proceedings of the 14th international conference on Medical image computing and computer-assisted intervention - Volume Part III
Superpixel-Based interest points for effective bags of visual words medical image retrieval
MCBR-CDS'11 Proceedings of the Second MICCAI international conference on Medical Content-Based Retrieval for Clinical Decision Support
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In vivo pathology from endomicroscopy videos can be a challenge for many physicians. To ease this task, we propose a content-based video retrieval method providing, given a query video, relevant similar videos from an expert-annotated database. Our main contribution consists in revisiting the Bag of Visual Words method by weighting the contributions of the dense local regions according to the registration results of mosaicing. We perform a leave-one-patient-out k-nearest neighbors classification and show a significantly better accuracy (e.g. around 94% for 9 neighbors) when compared to using the video images independently. Less neighbors are needed to classify the queries and our signature summation technique reduces retrieval runtime.