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International Journal for Research Trends and Innovation
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Paper Title: Detection of underwater images of fish-using feature learning technique
Unique Id: IJRTI1705059
Published In: Volume 2 Issue 5, May-2017
Abstract: Live fish acknowledgment is a standout amongst the most vital component of fisheries review applications. In fisheries overview handle a lot of information are quickly obtained. Not quite the same as general situations, difficulties to submerged picture acknowledgment are posted by poor picture quality, uncontrolled question and condition and additionally trouble in obtaining agent tests. Additionally, most existing element extraction methods are thwarted from computerization because of including human supervision. Towards this end, a submerged fish acknowledgment structure is suggested that comprise of a completely unsupervised element learning procedure and a mistake strong classifier and guarantee to get great picture quality. Protest parts introduced in view of saliency and unwinding naming to match question part accurately. A non-inflexible part model is then learned in light of wellness, division and separation criteria. MATLAB R2012b is favored for reenacting the said work. Some other MATLAB variants higher than MATLAB 2010 is prescribed because of its inserted with more numerical counts.
Keywords: Feature learning, fish species identification, protest acknowledgment, submerged symbolism, unsupervised learning
Cite Article: "Detection of underwater images of fish-using feature learning technique", International Journal of Science & Engineering Development Research (, ISSN:2455-2631, Vol.2, Issue 5, page no.317 - 319, May-2017, Available :
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Publication Details: Published Paper ID: IJRTI1705059
Registration ID:170284
Published In: Volume 2 Issue 5, May-2017
DOI (Digital Object Identifier):
ISSN Number: 2456 - 3315
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