Basic meanings of spatial relations: computation and evaluation in 3D space
AAAI'94 Proceedings of the twelfth national conference on Artificial intelligence (vol. 2)
Solving the multiple instance problem with axis-parallel rectangles
Artificial Intelligence
Closed-Loop Object Recognition Using Reinforcement Learning
IEEE Transactions on Pattern Analysis and Machine Intelligence
A framework for multiple-instance learning
NIPS '97 Proceedings of the 1997 conference on Advances in neural information processing systems 10
Fuzzy Relative Position Between Objects in Image Processing: A Morphological Approach
IEEE Transactions on Pattern Analysis and Machine Intelligence
Blobworld: Image Segmentation Using Expectation-Maximization and Its Application to Image Querying
IEEE Transactions on Pattern Analysis and Machine Intelligence
Extracting Subimages of an Unknown Category from a Set of Images
CVPR '06 Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 1
Object Localization Based on Directional Information: Case of 2D Raster Data
ICPR '06 Proceedings of the 18th International Conference on Pattern Recognition - Volume 02
A multiple instance learning based framework for semantic image segmentation
Multimedia Tools and Applications
Adaptive kernel diverse density estimate for multiple instance learning
MLDM'11 Proceedings of the 7th international conference on Machine learning and data mining in pattern recognition
Description and Discrimination of Planar Shapes Using Shape Matrices
IEEE Transactions on Pattern Analysis and Machine Intelligence
Weakly supervised learning of part-based spatial models for visual object recognition
ECCV'06 Proceedings of the 9th European conference on Computer Vision - Volume Part I
A Bayesian network-based tunable image segmentation algorithm for object recognition
ISSPIT '11 Proceedings of the 2011 IEEE International Symposium on Signal Processing and Information Technology
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Semantically accurate segmentation of an object of interest (OOI) is a critical step in computer vision tasks. In order to bridge the gap between low-level visual features and high-level semantics, a more complete model of the OOI is needed. To this end, we revise the concept of directional spatial templates and introduce regional directional spatial templates as a means of including spatial relationships among OOI regions into the model. We present an object segmentation algorithm that learns a model which includes both visual and spatial information. Given a training set of images containing the OOI, each image is oversegmented into visually homogeneous regions. Next, Multiple Instance Learning identifies regions that are likely to be part of the OOI. For each pair of such regions and for each relationship, a regional template is formed. The computational cost of template generation is reduced by sampling the reference region with a pixel set that is descriptive of its shape. Experiments indicate that regional templates are an effective way of including spatial information into the model which in turn results in a very significant improvement in segmentation performance.