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.
[1]
Mahdi Jalali, Department of Electrical Engineering, Naghadeh Branch, Islamic Azad University, Naghadeh, Iran.
[2]
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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