Grid-based local feature bundling for efficient object search and localization
We propose a new grid-based image representation for dis- criminative visual object search, with the goal to efficiently locate the query object in a large image collection. After ex- tracting local invariant features, we partition the image into non-overlapped rectangular grid cells. Each grid...
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Main Authors: | , , |
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其他作者: | |
格式: | Conference or Workshop Item |
語言: | English |
出版: |
2013
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主題: | |
在線閱讀: | https://hdl.handle.net/10356/100403 http://hdl.handle.net/10220/18118 |
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機構: | Nanyang Technological University |
語言: | English |
總結: | We propose a new grid-based image representation for dis-
criminative visual object search, with the goal to efficiently
locate the query object in a large image collection. After ex-
tracting local invariant features, we partition the image into
non-overlapped rectangular grid cells. Each grid bundles the
local features within it and is characterized by a histogram of
visual words. Given both positive and negative queries, each
grid is assigned a mutual information score to match and lo-
cate the query object. This new image representation brings
in two great benefits for efficient object search: 1) as the grid
bundles local features, the spatial contextual information en-
hances the discriminative matching; and 2) it enables faster
object localization by searching visual object on the grid-level
image. To evaluate our approach, we perform experiments on
a very challenging logo database BelgaLogos [1] of 10,000
images. The comparison with the state-of-the-art methods
highlights the effectiveness of our approach in both accuracy
and speed. |
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