A Graduated Assignment Algorithm for Graph Matching
IEEE Transactions on Pattern Analysis and Machine Intelligence
Histogram clustering for unsupervised segmentation and image retrieval
Pattern Recognition Letters
Blobworld: Image Segmentation Using Expectation-Maximization and Its Application to Image Querying
IEEE Transactions on Pattern Analysis and Machine Intelligence
Toward Global Solution to MAP Image Estimation: Using Common Structure of Local Solutions
EMMCVPR '97 Proceedings of the First International Workshop on Energy Minimization Methods in Computer Vision and Pattern Recognition
EMMCVPR '99 Proceedings of the Second International Workshop on Energy Minimization Methods in Computer Vision and Pattern Recognition
Indoor-Outdoor Image Classification
CAIVD '98 Proceedings of the 1998 International Workshop on Content-Based Access of Image and Video Databases (CAIVD '98)
Configuration based scene classification and image indexing
CVPR '97 Proceedings of the 1997 Conference on Computer Vision and Pattern Recognition (CVPR '97)
Statistical Models for Co-occurrence Data
Statistical Models for Co-occurrence Data
Image classification for content-based indexing
IEEE Transactions on Image Processing
ACM attributed graph clustering for learning classes of images
GbRPR'03 Proceedings of the 4th IAPR international conference on Graph based representations in pattern recognition
Graph matching and clustering using kernel attributes
Neurocomputing
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In this paper we address the problem of recognizing scenes by performing unsupervised segmentation followed by matching the resulting adjacency region graph. Our segmentation method is an adaptive extension of the Asymetric Clustering Model, a distributional clustering method based on the EM algorithm, whereas our matching proposal consists of embodying the Graduated Assignement cost function in a Comb Algorithm modified to perform constrained optimization in a discrete space. We present both segmentation and matching results that support our initial claim indicating that such an strategy provides both class discrimination and individual-within-a-class discrimination in indoor images which usually exhibit a high degree of perceptual ambiguity.