Cats And Dogs

We investigate the fine grained object categorization problem of determining the breed of animal from an image. To this end we introduce a new annotated dataset of pets covering 37 different breeds of cats and dogs. The visual problem is very challenging as these animals, particularly cats, are very...

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Main Authors: Parkhi, O, Vedaldi, A, Zisserman, A, Jawahar, C, IEEE
Format: Journal article
Language:English
Published: 2012
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author Parkhi, O
Vedaldi, A
Zisserman, A
Jawahar, C
IEEE
author_facet Parkhi, O
Vedaldi, A
Zisserman, A
Jawahar, C
IEEE
author_sort Parkhi, O
collection OXFORD
description We investigate the fine grained object categorization problem of determining the breed of animal from an image. To this end we introduce a new annotated dataset of pets covering 37 different breeds of cats and dogs. The visual problem is very challenging as these animals, particularly cats, are very deformable and there can be quite subtle differences between the breeds. We make a number of contributions: first, we introduce a model to classify a pet breed automatically from an image. The model combines shape, captured by a deformable part model detecting the pet face, and appearance, captured by a bag-of-words model that describes the pet fur. Fitting the model involves automatically segmenting the animal in the image. Second, we compare two classification approaches: a hierarchical one, in which a pet is first assigned to the cat or dog family and then to a breed, and a flat one, in which the breed is obtained directly. We also investigate a number of animal and image orientated spatial layouts. These models are very good: they beat all previously published results on the challenging ASIRRA test (cat vs dog discrimination). When applied to the task of discriminating the 37 different breeds of pets, the models obtain an average accuracy of about 59%, a very encouraging result considering the difficulty of the problem. © 2012 IEEE.
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spelling oxford-uuid:4f79662d-2e2d-4cc4-92e1-90419eea623b2022-03-26T16:07:30ZCats And DogsJournal articlehttp://purl.org/coar/resource_type/c_dcae04bcuuid:4f79662d-2e2d-4cc4-92e1-90419eea623bEnglishSymplectic Elements at Oxford2012Parkhi, OVedaldi, AZisserman, AJawahar, CIEEEWe investigate the fine grained object categorization problem of determining the breed of animal from an image. To this end we introduce a new annotated dataset of pets covering 37 different breeds of cats and dogs. The visual problem is very challenging as these animals, particularly cats, are very deformable and there can be quite subtle differences between the breeds. We make a number of contributions: first, we introduce a model to classify a pet breed automatically from an image. The model combines shape, captured by a deformable part model detecting the pet face, and appearance, captured by a bag-of-words model that describes the pet fur. Fitting the model involves automatically segmenting the animal in the image. Second, we compare two classification approaches: a hierarchical one, in which a pet is first assigned to the cat or dog family and then to a breed, and a flat one, in which the breed is obtained directly. We also investigate a number of animal and image orientated spatial layouts. These models are very good: they beat all previously published results on the challenging ASIRRA test (cat vs dog discrimination). When applied to the task of discriminating the 37 different breeds of pets, the models obtain an average accuracy of about 59%, a very encouraging result considering the difficulty of the problem. © 2012 IEEE.
spellingShingle Parkhi, O
Vedaldi, A
Zisserman, A
Jawahar, C
IEEE
Cats And Dogs
title Cats And Dogs
title_full Cats And Dogs
title_fullStr Cats And Dogs
title_full_unstemmed Cats And Dogs
title_short Cats And Dogs
title_sort cats and dogs
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AT vedaldia catsanddogs
AT zissermana catsanddogs
AT jawaharc catsanddogs
AT ieee catsanddogs