We propose a novel interest point detector stemming from the intuition that image patches which are highly dissimilar over a relatively large extent of their surroundings hold the property of being repeatable and distinctive. This concept of contextual self-dissimilarity reverses the key paradigm of recent successful techniques such as the Local Self-Similarity descriptor and the Non-Local Means filter, which build upon the presence of similar - rather than dissimilar - patches. Moreover, our approach extends to contextual information the local selfdissimilarity notion embedded in established detectors of corner-like interest points, thereby achieving enhanced repeatability, distinctiveness and localization accuracy.
CITATION STYLE
Tombari, F., & Di Stefano, L. (2015). Interest points via maximal self-dissimilarities. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 9004, pp. 586–600). Springer Verlag. https://doi.org/10.1007/978-3-319-16808-1_39
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