Three-dimensional object recognition from single two-dimensional images
Artificial Intelligence
The appeal of parallel distributed processing
Parallel distributed processing: explorations in the microstructure of cognition, vol. 1
Robust Contour Decomposition Using a Constant Curvature Criterion
IEEE Transactions on Pattern Analysis and Machine Intelligence
On Optimal Infinite Impulse Response Edge Detection Filters
IEEE Transactions on Pattern Analysis and Machine Intelligence
Explanation and artificial neural networks
International Journal of Man-Machine Studies
The nature of statistical learning theory
The nature of statistical learning theory
Data mining: practical machine learning tools and techniques with Java implementations
Data mining: practical machine learning tools and techniques with Java implementations
Finding Perceptually Closed Paths in Sketches and Drawings
IEEE Transactions on Pattern Analysis and Machine Intelligence
Measuring Shape: Ellipticity, Rectangularity, and Triangularity
ICPR '00 Proceedings of the International Conference on Pattern Recognition - Volume 1
Gestalt-based feature similarity measure in trademark database
Pattern Recognition
Shape feature matching for trademark image retrieval
CIVR'03 Proceedings of the 2nd international conference on Image and video retrieval
Estimating continuous distributions in Bayesian classifiers
UAI'95 Proceedings of the Eleventh conference on Uncertainty in artificial intelligence
Eliciting perceptual ground truth for image segmentation
CIVR'06 Proceedings of the 5th international conference on Image and Video Retrieval
Layout indexing of trademark images
Proceedings of the 6th ACM international conference on Image and video retrieval
Identifying perceptual structures in trademark images
SPPRA '08 Proceedings of the Fifth IASTED International Conference on Signal Processing, Pattern Recognition and Applications
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In this paper, we develop a system to classify the outputs of image segmentation algorithms as perceptually relevant or perceptually irrelevant with respect to human perception. The work is aimed at figurative images. We previously investigated human visual perception of trademark images and established a body of ground truth data in the form of trademark images and their respective human segmentations. The work indicated that there is a core set of segmentations for each image that people perceive. Here we use this core set of segmentations to train a classifier to classify closed shapes output from an image segmentation algorithm so that the method returns the image segments that match those produced by people. We demonstrate that a perceptual relevance classifier is attainable and identify a good methodology to achieve this. The paper compares MLP, SVM, Bayes and regression classifiers for classifying shapes. MLPs perform best with an overall accuracy of 96.4%.