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Proceedings Paper

Object recognition based on Google's reverse image search and image similarity
Author(s): András Horváth
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Paper Abstract

Image classification is one of the most challenging tasks in computer vision and a general multiclass classifier could solve many different tasks in image processing. Classification is usually done by shallow learning for predefined objects, which is a difficult task and very different from human vision, which is based on continuous learning of object classes and one requires years to learn a large taxonomy of objects which are not disjunct nor independent. In this paper I present a system based on Google image similarity algorithm and Google image database, which can classify a large set of different objects in a human like manner, identifying related classes and taxonomies.

Paper Details

Date Published: 9 December 2015
PDF: 5 pages
Proc. SPIE 9817, Seventh International Conference on Graphic and Image Processing (ICGIP 2015), 98170Q (9 December 2015); doi: 10.1117/12.2228505
Show Author Affiliations
András Horváth, Pázmány Péter Catholic Univ. (Hungary)

Published in SPIE Proceedings Vol. 9817:
Seventh International Conference on Graphic and Image Processing (ICGIP 2015)
Yulin Wang; Xudong Jiang, Editor(s)

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