IEEE Trans Pattern Anal Mach Intell - LDAHash: Improved Matching with Smaller Descriptors.

Tópicos

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{ perform(999) metric(946) measur(919) }
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{ medic(1828) order(1363) alert(1069) }
{ signal(2180) analysi(812) frequenc(800) }
{ group(2977) signific(1463) compar(1072) }
{ gene(2352) biolog(1181) express(1162) }
{ intervent(3218) particip(2042) group(1664) }
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{ drug(1928) target(777) effect(648) }
{ survey(1388) particip(1329) question(1065) }
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{ activ(1452) weight(1219) physic(1104) }
{ method(2212) result(1239) propos(1039) }

Resumo

SIFT-like local feature descriptors are ubiquitously employed in computer vision applications such as content-based retrieval, video analysis, copy detection, object recognition, photo tourism, and 3D reconstruction. Feature descriptors can be designed to be invariant to certain classes of photometric and geometric transformations, in particular, affine and intensity scale transformations. However, real transformations that an image can undergo can only be approximately modeled in this way, and thus most descriptors are only approximately invariant in practice. Second, descriptors are usually high dimensional (e.g., SIFT is represented as a 128-dimensional vector). In large-scale retrieval and matching problems, this can pose challenges in storing and retrieving descriptor data. We map the descriptor vectors into the Hamming space in which the Hamming metric is used to compare the resulting representations. This way, we reduce the size of the descriptors by representing them as short binary strings and learn descriptor invariance from examples. We show extensive experimental validation, demonstrating the advantage of the proposed approach.

Resumo Limpo

siftlik local featur descriptor ubiquit employ comput vision applic contentbas retriev video analysi copi detect object recognit photo tourism d reconstruct featur descriptor can design invari certain class photometr geometr transform particular affin intens scale transform howev real transform imag can undergo can approxim model way thus descriptor approxim invari practic second descriptor usual high dimension eg sift repres dimension vector largescal retriev match problem can pose challeng store retriev descriptor data map descriptor vector ham space ham metric use compar result represent way reduc size descriptor repres short binari string learn descriptor invari exampl show extens experiment valid demonstr advantag propos approach

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