Assessment of Cow’s Body Condition Score Through Statistical Shape Analysis and Regression Machines

Sebastiano Battiato, Giovanni Maria Farinella, Giuseppe Claudio Guarnera, Giovanni Puglisi, Giuseppe Azzaro, Margherita Caccamo
Proceedings of the First Workshop on Applications of Pattern Analysis, PMLR 11:66-73, 2010.

Abstract

This study explores the feasibility of estimating the Body Condition Score (BCS) of cows from digital images by employing statistical shape analysis and regression machines. The shapes of body cows are described through a number of variations from a unique average shape. Specifically, Kernel Principal Component Analysis is used to determine the components describing the many ways in which the body shape of different cows tend to deform from the average shape. This description is used for automatic estimation of BCS through regression approach. The proposed method has been tested on a new benchmark dataset available through the Internet. Experimental results confirm the effectiveness of the proposed technique that outperforms the state-of-the-art approaches proposed in the context of dairy cattle research.

Cite this Paper

BibTeX
@InProceedings{pmlr-v11-battiato10a, title = {Assessment of Cow's Body Condition Score Through Statistical Shape Analysis and Regression Machines}, author = {Battiato, Sebastiano and Farinella, Giovanni Maria and Guarnera, Giuseppe Claudio and Puglisi, Giovanni and Azzaro, Giuseppe and Caccamo, Margherita}, booktitle = {Proceedings of the First Workshop on Applications of Pattern Analysis}, pages = {66--73}, year = {2010}, editor = {Diethe, Tom and Cristianini, Nello and Shawe-Taylor, John}, volume = {11}, series = {Proceedings of Machine Learning Research}, address = {Cumberland Lodge, Windsor, UK}, month = {01--03 Sep}, publisher = {PMLR}, pdf = {http://proceedings.mlr.press/v11/battiato10a/battiato10a.pdf}, url = {https://proceedings.mlr.press/v11/battiato10a.html}, abstract = {This study explores the feasibility of estimating the Body Condition Score (BCS) of cows from digital images by employing statistical shape analysis and regression machines. The shapes of body cows are described through a number of variations from a unique average shape. Specifically, Kernel Principal Component Analysis is used to determine the components describing the many ways in which the body shape of different cows tend to deform from the average shape. This description is used for automatic estimation of BCS through regression approach. The proposed method has been tested on a new benchmark dataset available through the Internet. Experimental results confirm the effectiveness of the proposed technique that outperforms the state-of-the-art approaches proposed in the context of dairy cattle research.} }
Endnote
%0 Conference Paper %T Assessment of Cow’s Body Condition Score Through Statistical Shape Analysis and Regression Machines %A Sebastiano Battiato %A Giovanni Maria Farinella %A Giuseppe Claudio Guarnera %A Giovanni Puglisi %A Giuseppe Azzaro %A Margherita Caccamo %B Proceedings of the First Workshop on Applications of Pattern Analysis %C Proceedings of Machine Learning Research %D 2010 %E Tom Diethe %E Nello Cristianini %E John Shawe-Taylor %F pmlr-v11-battiato10a %I PMLR %P 66--73 %U https://proceedings.mlr.press/v11/battiato10a.html %V 11 %X This study explores the feasibility of estimating the Body Condition Score (BCS) of cows from digital images by employing statistical shape analysis and regression machines. The shapes of body cows are described through a number of variations from a unique average shape. Specifically, Kernel Principal Component Analysis is used to determine the components describing the many ways in which the body shape of different cows tend to deform from the average shape. This description is used for automatic estimation of BCS through regression approach. The proposed method has been tested on a new benchmark dataset available through the Internet. Experimental results confirm the effectiveness of the proposed technique that outperforms the state-of-the-art approaches proposed in the context of dairy cattle research.
RIS
TY - CPAPER TI - Assessment of Cow’s Body Condition Score Through Statistical Shape Analysis and Regression Machines AU - Sebastiano Battiato AU - Giovanni Maria Farinella AU - Giuseppe Claudio Guarnera AU - Giovanni Puglisi AU - Giuseppe Azzaro AU - Margherita Caccamo BT - Proceedings of the First Workshop on Applications of Pattern Analysis DA - 2010/09/30 ED - Tom Diethe ED - Nello Cristianini ED - John Shawe-Taylor ID - pmlr-v11-battiato10a PB - PMLR DP - Proceedings of Machine Learning Research VL - 11 SP - 66 EP - 73 L1 - http://proceedings.mlr.press/v11/battiato10a/battiato10a.pdf UR - https://proceedings.mlr.press/v11/battiato10a.html AB - This study explores the feasibility of estimating the Body Condition Score (BCS) of cows from digital images by employing statistical shape analysis and regression machines. The shapes of body cows are described through a number of variations from a unique average shape. Specifically, Kernel Principal Component Analysis is used to determine the components describing the many ways in which the body shape of different cows tend to deform from the average shape. This description is used for automatic estimation of BCS through regression approach. The proposed method has been tested on a new benchmark dataset available through the Internet. Experimental results confirm the effectiveness of the proposed technique that outperforms the state-of-the-art approaches proposed in the context of dairy cattle research. ER -
APA
Battiato, S., Farinella, G.M., Guarnera, G.C., Puglisi, G., Azzaro, G. & Caccamo, M.. (2010). Assessment of Cow’s Body Condition Score Through Statistical Shape Analysis and Regression Machines. Proceedings of the First Workshop on Applications of Pattern Analysis, in Proceedings of Machine Learning Research 11:66-73 Available from https://proceedings.mlr.press/v11/battiato10a.html.

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