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Paper Details

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Fish Species Identification in Real-Life Underwater Images


Kernel descriptors consist in finite-dimensional vectors extracted from image patches and designed in such a way that the dot product approximates a nonlinear kernel, whose projection feature space would be high-dimensional. Recently, they have been successfully used for fine-gradined object recogntion, and in this work we study the application of two such descriptors, called EMK and KDES (respectively designed as a kernelized generalization of the common bag-of-words and histogram-of-gradient approaches) to the MAED 2014 Fish Classification task, consisting of about 50,000 underwater images from 10 fish species.

"Nature is the source of all true knowledge. She has her own logic, her own laws,
she has no effect without cause nor invention without necessity." L. Da Vinci."