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Learning object classes from structure

Xiao, B., Song, Yi-Zhe and Hall, P.M. (2007) Learning object classes from structure In: British Machine Conference (BMVC 2007), 10-13 Sep 2007, Warwick, UK.

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Abstract

The problem of identifying the class of an object from its visual appearance has received significant attention recently. Most of the work to date is premised on photometric measures, often building codebooks made from interest regions. All of it has been tested only on photographs, so far as we know. Our approach differs in two significant ways. First, we do not build a codebook of interest regions but instead make use of a hierarchical description of an image based on a watershed transform. Root nodes in the hierarchy are putative objects to be classified. Second, we classify these putative objects using a vector of fixed length that represents the structure of the hierarchy below the node. This allows us to classify not just photographs, but also paintings and drawings of visual objects.

Item Type: Conference or Workshop Item (Conference Paper)
Divisions : Faculty of Engineering and Physical Sciences > Electronic Engineering
Authors :
NameEmailORCID
Xiao, B.
Song, Yi-Zhey.song@surrey.ac.uk
Hall, P.M.
Date : September 2007
DOI : 10.5244/C.21.27
Related URLs :
Depositing User : Clive Harris
Date Deposited : 13 Aug 2019 13:28
Last Modified : 13 Aug 2019 13:28
URI: http://epubs.surrey.ac.uk/id/eprint/852162

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