Shape Similarity Measure Based on Correspondence of Visual Parts
IEEE Transactions on Pattern Analysis and Machine Intelligence
A Multistep Approach for Shape Similarity Search in Image Databases
IEEE Transactions on Knowledge and Data Engineering
Shape Matching and Object Recognition Using Shape Contexts
IEEE Transactions on Pattern Analysis and Machine Intelligence
IEEE Transactions on Pattern Analysis and Machine Intelligence
Shape Matching: Similarity Measures and Algorithms
SMI '01 Proceedings of the International Conference on Shape Modeling & Applications
A similarity metric for edge images
IEEE Transactions on Pattern Analysis and Machine Intelligence
Object-based image similarity computation using inductive learning of contour-segment relations
IEEE Transactions on Image Processing
Distance sets for shape filters and shape recognition
IEEE Transactions on Image Processing
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In this paper, we propose a tree-based approach to represent and compare image objects. Upon objects separated from images trees are constructed. The key observation is that from similar objects similar trees are produced. On the other hand, upon dissimilar objects unlike trees are created. Additionally, the degree of dissimilarity between objects is proportional to the degree of dissimilarity between the trees. Hence, it is possible to express the difference between two objects as the difference between the trees. The paper presents algorithms of creating and comparing trees as well as results, which confirm usefulness of the approach.