Algorithms for clustering data
Algorithms for clustering data
Making large-scale support vector machine learning practical
Advances in kernel methods
A Tutorial on Support Vector Machines for Pattern Recognition
Data Mining and Knowledge Discovery
A generalized metric distance between hierarchically partitioned images
MDM '05 Proceedings of the 6th international workshop on Multimedia data mining: mining integrated media and complex data
A cartography of spatial relationships in a symbolic image database
CAIP'11 Proceedings of the 14th international conference on Computer analysis of images and patterns - Volume Part I
Combining neural networks and clustering techniques for object recognition in indoor video sequences
CIARP'06 Proceedings of the 11th Iberoamerican conference on Progress in Pattern Recognition, Image Analysis and Applications
CIARP'05 Proceedings of the 10th Iberoamerican Congress conference on Progress in Pattern Recognition, Image Analysis and Applications
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Over-segmentation could be relieved by adopting a divisive image segmentation model. This also requires the binary classification of whether a segmented region corresponds to a single semantic object. In this paper, we propose a model to address this classification problem, by detecting if a region contains both "background" and "foreground" regions. When "background" and "foreground" both present, the region is considered to have multiple objects, otherwise it corresponds to a single object. We implement the model based on certain image features of the region that effectively tell the difference between "background" and "foreground". Experiments show that our model can effectively perform the classification tasks.