Good recognition is non-metric
作者： Walter J Scheirer , Michael J Wilber , Michael Eckmann , Terrance E Boult , None
DOI: 10.1016/J.PATCOG.2014.02.018
关键词:
摘要: Abstract Recognition is the fundamental task of visual cognition, yet how to formalize general recognition problem for computer vision remains an open issue. The sometimes reduced simplest case recognizing matching pairs, often structured allow metric constraints. However, broader than just pair-matching: what we learn and it has important implications effective algorithms. In this review paper, reconsider assumption as a pair-matching test, introduce new formal definition that captures context problem. Through meta-analysis experimental assessment top algorithms on popular data sets, gain sense properties are violated by By studying these violations, useful insights come light: make local distances systems leverage outside information solve
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