Volume & Issue no: Volume 6, Issue 4, July - August 2017
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Title: |
Unsupervised Learning of Semantic Classes for Image Mining |
Author Name: |
K Rajendra Prasad |
Abstract: |
Abstract
Scalability is one of key aspect to effective use of retrieval of
image annotation in image mining. Exploiting semantic
relations can significantly improve the scalability of SIMM for
large-scale datasets. The overall performance of proposed
SIMM is compared with two exiting methods, k-means and
spectral clustering with fuzzy concepts. Efficiency of SIMM is
demonstrated with two parameters, accuracy and normalized
mutual information on real world image databases during
experimental study. The objective is to learn fine-grained
distinctions among images and produce a fuzzy based similarity
score for developing an effective Semantic based Image-Mining
Method (SIMM).
Keywords:Image annotation, semantic map, fuzzy
concept, PSA, SIMM |
Cite this article: |
K Rajendra Prasad , "
Unsupervised Learning of Semantic Classes for Image Mining" , International Journal of Emerging Trends & Technology in Computer Science (IJETTCS) ,
Volume 6, Issue 4, July - August 2017 , pp.
088-090 , ISSN 2278-6856.
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