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A Feature Learning and Object Recognition Framework for Underwater Fish Images

A Feature Learning and Object Recognition Framework for Underwater Fish Images

Starting at: Rs.5,500.00

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 A Feature Learning and Object Recognition Framework for Underwater Fish Images

Live fish recognition is one of the most crucial elements of fisheries survey applications where the vast amount of data is rapidly acquired. Different from general scenarios, challenges to underwater image recognition are posted by poor image quality, uncontrolled objects and environment, and dif-ficulty in acquiring representative samples. In addition, most existing feature extraction techniques are hindered from automa-tion due to involving human supervision. Toward this end, we propose an underwater fish recognition framework that consists of a fully unsupervised feature learning technique and an error-resilient classifier. Object parts are initialized based on saliency and relaxation labeling to match object parts correctly. A non-rigid part model is then learned based on fitness, separation, and discrimination criteria. For the classifier, an unsupervised clustering approach generates a binary class hier-archy, where each node is a classifier. To exploit information from ambiguous images, the notion of partial classification is introduced to assign coarse labels by optimizing the benefit of indecision made by the classifier. Experiments show that the proposed framework achieves high accuracy on both public and self-collected underwater fish images with high uncertainty and class imbalance.


 


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  • Model: PROJ7587
  • 999 Units in Stock
  • Manufactured by: ClickMyProjects

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This product was added to our catalog on Monday 07 August, 2017.

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