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AASCIT Communications | Volume 2, Issue 5 | Jul. 21, 2015 online | Page:191-194
Semantic Gap Reduction in Semi-Supervised Classification by Different Feature Composition
In this paper multiple feature combination are generated for reduction of semantic gap under supervised classification. This means that the comparison in image retrieval is done once feature generation, and it is the supervised classification and a unique pattern of images to verify semantic gap. It is the same as using the supervised classification algorithm to classify functions as a set of various branches formed. Observations show that the images used to recall systems are safer and more reliable than the previously published papers. The cards could provide reliable retrieval systems. With image readers to reduce costs and increase the power of low-cost computers, automatic image recognition is an effective and inexpensive alternative to regular solutions to reduce semantic gap.
Mahdi Jalali, Department of Electrical Engineering, Naghadeh Branch, Islamic Azad University, Naghadeh, Iran.
Tohid Sedghi, Department of Electrical Engineering, Urmia Branch, Islamic Azad University, Urmia, Iran.
Feature Generation, Feature Extraction, Supervised Classification, Image Indexing, Neighbor-Hood
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Mahdi Jalali, “Multi-Scale Recognition of Objects Approach based on Inherent Redundancy Information Entropy Equalization” Research and Reviews: Journal of Engineering and Technology, RRJET, Volume 3, Issue 1, January - March, 2012
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Mahdi Jalali, "Efficient Color Histogram Relationship Matching Approach Based on Absolute Heavily Dependent Spatial Patterns", International Journal of Engineering & Technology Sciences (IJETS) 1(2): 96-99, 2013
Sedghi T., “A Fast and Effective Model for cyclic Analysis and its application in classification” Arabian Journal for Science and Engineering Vol. 38 October 2012.
Arcticle History
Submitted: Oct. 10, 2014
Accepted: May 31, 2015
Published: Jul. 21, 2015
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