Fish species recognition by shape analysis of images
Pattern Recognition
Recognition of fish species by colour and shape
Image and Vision Computing
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IEEE Transactions on Pattern Analysis and Machine Intelligence
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EuroCOLT '95 Proceedings of the Second European Conference on Computational Learning Theory
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ACM SIGGRAPH 2004 Papers
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ICPR '04 Proceedings of the Pattern Recognition, 17th International Conference on (ICPR'04) Volume 2 - Volume 02
Shape and Texture Based Classification of Fish Species
SCIA '09 Proceedings of the 16th Scandinavian Conference on Image Analysis
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Proceedings of the first ACM international workshop on Analysis and retrieval of tracked events and motion in imagery streams
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Data Mining and Knowledge Discovery
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ACM Transactions on Intelligent Systems and Technology (TIST)
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Proceedings of the 2nd ACM international workshop on Multimedia analysis for ecological data
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Live fish recognition in the open sea is a challenging multi-class classification task. We propose a novel method to recognize fish in an unrestricted natural environment recorded by underwater cameras. This method extracts 66 types of features, which are a combination of color, shape and texture properties from different parts of the fish and reduce the feature dimensions with forward sequential feature selection (FSFS) procedure. The selected features of the FSFS are used by an SVM. We present a Balance-Guaranteed Optimized Tree (BGOT) to control the error accumulation in hierarchical classification and, therefore, achieve better performance. A BGOT of 10 fish species is automatically constructed using the inter-class similarities and a heuristic method. The proposed BGOT-based hierarchical classification method achieves about 4% better accuracy compared to state-of-the-art techniques on a live fish image dataset.