Inferring global perceptual contours from local features
International Journal of Computer Vision - Special issue on computer vision research at the University of Southern California
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
Normalized Cuts and Image Segmentation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Digital Image Processing
Blobworld: Image Segmentation Using Expectation-Maximization and Its Application to Image Querying
IEEE Transactions on Pattern Analysis and Machine Intelligence
Composing cardinal direction relations
Artificial Intelligence
Discovering Objects and their Localization in Images
ICCV '05 Proceedings of the Tenth IEEE International Conference on Computer Vision (ICCV'05) Volume 1 - Volume 01
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
Weakly Supervised Scale-Invariant Learning of Models for Visual Recognition
International Journal of Computer Vision
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
Automatic image segmentation by integrating color-edge extraction and seeded region growing
IEEE Transactions on Image Processing
A multiscale random field model for Bayesian image segmentation
IEEE Transactions on Image Processing
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Semantically accurate segmentation of a particular Object Of Interest (OOI) in an image is an important but challenging step in computer vision tasks. Our recently proposed object-specific segmentation algorithm learns a model of the OOI which includes information on both the visual appearance of and the spatial relationships among the OOI components. However, its performance heavily depends on the assumption that the visual appearance variability among OOI instances is low. We present an extension to our algorithm that relaxes this assumption by incorporating shape information into the OOI model. Experimental results and an ANOVA-based statistical test confirm that the incorporation of shape has a highly significant positive effect on segmentation performance.