Gabor descriptors for aerial image classification
ICANNGA'11 Proceedings of the 10th international conference on Adaptive and natural computing algorithms - Volume Part II
Efficient retrieval of 3D building models using embeddings of attributed subgraphs
Proceedings of the 20th ACM international conference on Information and knowledge management
Frequent approximate subgraphs as features for graph-based image classification
Knowledge-Based Systems
A new proposal for graph classification using frequent geometric subgraphs
Data & Knowledge Engineering
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We describe an image representation that combines the representational power of graphs with the efficiency of the bag-of-words model. For each image in a data set, first, a graph is constructed from local patches of interest regions and their spatial arrangements. Then, each graph is represented with a histogram of sub graphs selected using a frequent subgraph mining algorithm in the whole data. Using the sub graphs as the visual words of the bag-of-words model and transforming of the graphs into a vector space using this model enables statistical classification of images using support vector machines. Experiments using images cut from a large satellite scene show the effectiveness of the proposed representation in classification of complex types of scenes into eight high-level semantic classes.