A Computational Approach to Edge Detection
IEEE Transactions on Pattern Analysis and Machine Intelligence
Contextual decision rule for region analysis
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A Rule Based Approach for Visual Pattern Inspection
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A Transform for Multiscale Image Segmentation by Integrated Edge and Region Detection
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A Graduated Assignment Algorithm for Graph Matching
IEEE Transactions on Pattern Analysis and Machine Intelligence
Closed-Loop Object Recognition Using Reinforcement Learning
IEEE Transactions on Pattern Analysis and Machine Intelligence
Recognition Using Region Correspondences
International Journal of Computer Vision
An energy function and continuous edit process for graph matching
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Matching Hierarchical Structures Using Association Graphs
IEEE Transactions on Pattern Analysis and Machine Intelligence
Shock Graphs and Shape Matching
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Mean and maximum common subgraph of two graphs
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Normalized Cuts and Image Segmentation
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Object Recognition Using Shape-from-Shading
IEEE Transactions on Pattern Analysis and Machine Intelligence
Scale-Space Theory in Computer Vision
Scale-Space Theory in Computer Vision
Matching Free Trees, Maximal Cliques, and Monotone Game Dynamics
IEEE Transactions on Pattern Analysis and Machine Intelligence
Class-Specific, Top-Down Segmentation
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Matching and Embedding through Edit-Union of Trees
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Region-Based Object Recognition Using Shape-from-Shading
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Many-to-many Matching of Attributed Trees Using Association Graphs and Game Dynamics
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Recognition of Shapes by Editing Their Shock Graphs
IEEE Transactions on Pattern Analysis and Machine Intelligence
Learning to Detect Natural Image Boundaries Using Local Brightness, Color, and Texture Cues
IEEE Transactions on Pattern Analysis and Machine Intelligence
Distinctive Image Features from Scale-Invariant Keypoints
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Learning to Detect Objects in Images via a Sparse, Part-Based Representation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Object Recognition with Features Inspired by Visual Cortex
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Indexing Hierarchical Structures Using Graph Spectra
IEEE Transactions on Pattern Analysis and Machine Intelligence
Recognizing Articulated Objects Using a Region-Based Invariant Transform
IEEE Transactions on Pattern Analysis and Machine Intelligence
LOCUS: Learning Object Classes with Unsupervised Segmentation
ICCV '05 Proceedings of the Tenth IEEE International Conference on Computer Vision (ICCV'05) Volume 1 - Volume 01
Learning Hierarchical Models of Scenes, Objects, and Parts
ICCV '05 Proceedings of the Tenth IEEE International Conference on Computer Vision - Volume 2
Object Categorization by Learned Universal Visual Dictionary
ICCV '05 Proceedings of the Tenth IEEE International Conference on Computer Vision - Volume 2
One-Shot Learning of Object Categories
IEEE Transactions on Pattern Analysis and Machine Intelligence
A Comparison of Affine Region Detectors
International Journal of Computer Vision
Learning Shape-Classes Using a Mixture of Tree-Unions
IEEE Transactions on Pattern Analysis and Machine Intelligence
Object Recognition as Many-to-Many Feature Matching
International Journal of Computer Vision
Incremental learning of object detectors using a visual shape alphabet
CVPR '06 Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 1
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
Using Multiple Segmentations to Discover Objects and their Extent in Image Collections
CVPR '06 Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 2
Analysis of Ramp Discontinuity Model for Multiscale Image Segmentation
ICPR '06 Proceedings of the 18th International Conference on Pattern Recognition - Volume 04
Discovering Shape Classes using Tree Edit-Distance and Pairwise Clustering
International Journal of Computer Vision
Graph matching and clustering using spectral partitions
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Matching by Linear Programming and Successive Convexification
IEEE Transactions on Pattern Analysis and Machine Intelligence
LabelMe: A Database and Web-Based Tool for Image Annotation
International Journal of Computer Vision
Region-Based Hierarchical Image Matching
International Journal of Computer Vision
Unsupervised Category Modeling, Recognition, and Segmentation in Images
IEEE Transactions on Pattern Analysis and Machine Intelligence
Graph clustering using the weighted minimum common supergraph
GbRPR'03 Proceedings of the 4th IAPR international conference on Graph based representations in pattern recognition
Sharing features: efficient boosting procedures for multiclass object detection
CVPR'04 Proceedings of the 2004 IEEE computer society conference on Computer vision and pattern recognition
Hidden semantic concept discovery in region based image retrieval
CVPR'04 Proceedings of the 2004 IEEE computer society conference on Computer vision and pattern recognition
Multiscale image segmentation by integrated edge and region detection
IEEE Transactions on Image Processing
Hierarchical graph representation for symbol spotting in graphical document images
SSPR'12/SPR'12 Proceedings of the 2012 Joint IAPR international conference on Structural, Syntactic, and Statistical Pattern Recognition
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This paper presents an overview of the work we have done over the last several years on object recognition in images from region-based image representation. The overview focuses on the following related problems: (1) discovery of a single 2D object category frequently occurring in a given image set; (2) learning a model of the discovered category in terms of its photometric, geometric, and structural properties; and (3) detection and segmentation of objects from the category in new images. Images in the given set are segmented, and then each image is represented by a region graph that captures hierarchy and neighbor relations among image regions. The region graphs are matched to extract the maximally matching subgraphs, which are interpreted as instances of the discovered category. A graph-union of the matching subgraphs is taken as a model of the category. Matching the category model to the region graph of a new image yields joint object detection and segmentation. The paper argues that using a hierarchy of image regions and their neighbor relations offers a number of advantages in solving (1)-(3), over the more commonly used point and edge features. Experimental results, also reviewed in this paper, support the above claims. Details of our methods as well of comparisons with other methods are omitted here, and can be found in the indicated references.