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Learning Features for Identifying Dolphins

Luiz GoncalvesAdelardo MedeirosKaiser Magalde

出版	INTECH Open Access Publisher, 2006
主題	Technology & Engineering / Automation
ISBN	38661131459783866113145
URL	http://books.google.com.hk/books?id=qQa0oAEACAAJ&hl=&source=gbs_api

註釋In this work, we have proposed a complete system for identification of dolphins. The system involves image acquisition, pre-processing, feature extraction, and identification. The novelties of the system are mainly in the proposed methodology for pre-processing the images based on KLT for extraction of the dorsal fin, the new feature set proposed for being used as input to the classifiers, and the self-growing mechanism itself. With that, the system does not need user intervention for training its memory, neither for identifying new subjects in the universe of animals. The system has proceeded as expected in all experiments. Of course, other alternatives for the classifier can be tested in the following, for example, Bayesian nets, self organizing maps, or radial basis functions. Self organizing maps has the property of separating the input space in clusters. We do not know if this would be a good strategy, it has to be tested, perhaps for separation between families. It is important to remark that the feature set used can be enhanced. A good set must consider texture, besides shape features. In order to use texture, one must use an approach for avoiding water effects in the image illumination. There are some restrictions to be observed in the feature extraction. For example, in the case of substantial rotation, close to 90 degrees, the holes or cracks may not be visible. Holes may not be detected, prejudicing the system performance. Of course, a visual procedure in the considered picture would not produce any good result too, as in this case the fin may become visible from the front or from the back of the dolphin. So, besides the necessity of such improvements, we enhance the importance of the features proposed, which has produced good results. The BP net used showed to be useful in the insertion of new individuals in the long term memory. Yet, some strategy can be developed in order for the system to learn what features are the best to segregate individuals from a given group in a more precise way. If the group changes, the fins characteristics may also change. In this way, a smaller feature set can be used, diminishing training time and growing in efficiency. Also, using weights in the features is another strategy to be tried. The idea is to determine the most relevant features, for specie specifics. A stochastic inference approach can be tried, as future work, in this track.