Extraction of Multiple Hybrid Features to Reduce the Semantic Vacuum with the Semi-Supervised Classification

Q: How can I download an article?

To download an article from SID, first log in to the site, search for the article title, and click on the 'Download Article' option.

Q: How can I download an ISI article?

To download an ISI article on SID, enter the keyword or article title in the search bar, view the relevant results, click on the desired article, and select the 'Download Article' option.

Q: How can I access the SID database?

To access the SID database, visit SID.ir, create an account, and log in to access scientific resources.

Q: Is downloading articles from SID free?

Some articles on SID are available for free, while others require payment. Details are specified on the article's page.

JALALI MAHDI; SEDGHI TOHID

Scientific Information Database (SID) - Trusted Source for Research and Academic Resources

Journal Paper

Paper Information

Journal: JOURNAL OF COMMUNICATION ENGINEERING Year:2022 | Volume:12 | Issue:45 Page(s): 31-43

Download Full-Text

مرکز اطلاعات علمی Scientific Information Database (SID) - Trusted Source for Research and Academic Resources

Persian Verion

View:

544

Download:

Cites:

Information Journal Paper

Title

Extraction of Multiple Hybrid Features to Reduce the Semantic Vacuum with the Semi-Supervised Classification

Author(s)

JALALI MAHDI | SEDGHI TOHID | Issue Writer Certificate

Keywords

Semi-supervised classification

image retrieval

feature

annotation

Abstract

In this paper, for the classification of images, the observed cooperative classification method is proposed with the aim of reducing the semantic vacuum. Because most classification methods are sensitive to cluster centers at initialization, the algorithm converges optimally locally if the quantification is not done correctly. It is also very difficult to combine the results of the classification due to the fact that the labels of the work centers are not clear. Supervised semi-classification is used to solve these problems. To achieve the highest performance, the system classification results are combined with different color space and similarity criteria with multiple features as a semi-supervised cooperative. When the number of features is effective, the relevant feedback is used for its semi-regulatory classification. One of the most important parts of an image retrieval system and a classification algorithm is to determine the appropriate similarity between images. In this study, two methods of classification of cases have been used, which include k-NN and PNN classification, which according to the results in all proposed methods, a better response from k-NN classification than PNN has been observed. The proposed algorithm is very suitable for classifying large image databases due to the reduction of time complexity. Also, in image retrieval in different color spaces and using different similarity criteria, different classifications are obtained. Better results can be achieved if the classification results are combined. Recognition rate on the image data used in this study, on which the pyramidal histogram algorithm of oriented gradients is applied, has the highest rate compared to other proposed methods and is 98. 52%. Experiments on the Corel image database show that the combined method increases the classification accuracy by an average of about 20% in the combined method.

Cites

No record.

References

No record.

Cite

APA: Copy

JALALI, MAHDI, & SEDGHI, TOHID. (2022). Extraction of Multiple Hybrid Features to Reduce the Semantic Vacuum with the Semi-Supervised Classification. JOURNAL OF COMMUNICATION ENGINEERING, 12(45 ), 31-43. SID. https://sid.ir/paper/966650/en

Vancouver: Copy

JALALI MAHDI, SEDGHI TOHID. Extraction of Multiple Hybrid Features to Reduce the Semantic Vacuum with the Semi-Supervised Classification. JOURNAL OF COMMUNICATION ENGINEERING[Internet]. 2022;12(45 ):31-43. Available from: https://sid.ir/paper/966650/en

IEEE: Copy

MAHDI JALALI, and TOHID SEDGHI, “Extraction of Multiple Hybrid Features to Reduce the Semantic Vacuum with the Semi-Supervised Classification,” JOURNAL OF COMMUNICATION ENGINEERING, vol. 12, no. 45 , pp. 31–43, 2022, [Online]. Available: https://sid.ir/paper/966650/en

Related Journal Papers

No record.

Related Seminar Papers

No record.

Related Plans

No record.

Recommended Workshops