A novel features design method for cat head detection

Authors:
Hua Bo
Affiliations:
School of Information Enginnering, Shanghai Maritime University, Shanghai, China
Venue:
AICI'10 Proceedings of the 2010 international conference on Artificial intelligence and computational intelligence: Part I
Year:
2010

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Abstract

In this paper we have proposed a new novel features model which designed to robustly detect the highly variable cat head patterns. Do not like human, cats usually have distinct different face, pose, appearance and different scales of ears, eyes and mouth. So many significant features on human face detection have presented but itis not satisfying to use them on cat head. We have designed a new features model by ideally combining the histogram frame with GLCM-based (gray level co-occurrence matrix) texture features to describe both the shape information of cat's head and texture information of cat's eyes, ears and mouth in detail. SVM-based classifier achieves the detection results. Extensive experimental results illustrating the high detection rate with low false alarm.