Similarity Searching in Medical Image Databases
IEEE Transactions on Knowledge and Data Engineering
gSpan: Graph-Based Substructure Pattern Mining
ICDM '02 Proceedings of the 2002 IEEE International Conference on Data Mining
Taming verification hardness: an efficient algorithm for testing subgraph isomorphism
Proceedings of the VLDB Endowment
Corpus callosum MR image classification
Knowledge-Based Systems
Aggregated search in graph databases: preliminary results
GbRPR'11 Proceedings of the 8th international conference on Graph-based representations in pattern recognition
Graph-based approach for human action recognition using spatio-temporal features
Journal of Visual Communication and Image Representation
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With the rapid growth of video multimedia databases and the lack of textual descriptions for many of them, video annotation became a highly desired task. Conventional systems try to annotate a video query by simply finding its most similar videos in the database. Although the video annotation problem has been tackled in the last decade, no attention has been paid to the problem of assembling video keyframes in a sensed way to provide an answer of the given video query when no single candidate video turns out to be similar to the query. In this paper, we introduce a graph based image modeling and indexing system for video annotation. Our system is able to improve the video annotation task by assembling a set of graphs representing different keyframes of different videos, to compose the video query. The experimental results demonstrate the effectiveness of our system to annotate videos that are not possibly annotated by classical approaches.