An Efficient Multiple Convolutional Neural Network Model (MCNN-14) for Fashion Image 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.

Saed Sajjad; Teimourpour Babak; Kalashi Kamand; Soltanshahi Mohammad Ali

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

Seminar Paper

Paper Information

Seminar: INTERNATIONAL CONFERENCE ON WEB RESEARCH
Year:2024 | Issue:10

Download Full-Text

View:

Download:

Information Seminar Paper

Title

An Efficient Multiple Convolutional Neural Network Model (MCNN-14) for Fashion Image Classification

Author(s)

Saed Sajjad | Teimourpour Babak | Kalashi Kamand | Soltanshahi Mohammad Ali

Keywords

Deep Learning

Image Classification

Multiple Convolutional Neural Networks

Fashion-MNIST

Abstract

The transformation of fashion through online platforms has spurred a need for high-quality clothing search engines, facilitating seamless product discovery for global consumers. However, this transition has brought forth challenges in categorization and description standards among retailers and search engines, stemming from the inherent complexity and variability of fashion items. To address these challenges, Deep Learning techniques like Multiple Convolutional Neural Networks (MCNNs) have gained prominence in the fashion industry. MCNNs offer autonomous learning and enhanced feature extraction capabilities, proving successful in various classification tasks, including clothing classification. In our research, we implemented a multiple-CNN architecture inspired by recent architectural innovations to elevate classification accuracy while preserving computational efficiency. We attained a classification accuracy of 93. 08%, surpassing previous benchmarks. However, the integration of multiple CNNs introduces complexities such as heightened computational demands and resource requirements. To strike a balance between achieving superior accuracy and managing the inherent complexities in clothing Image Classification, we designed the MCNN-14 model.

Video

Cites

No record.

References

No record.

Cite

APA: Copy

Saed, Sajjad, Teimourpour, Babak, Kalashi, Kamand, & Soltanshahi, Mohammad Ali. (2024). An Efficient Multiple Convolutional Neural Network Model (MCNN-14) for Fashion Image Classification. INTERNATIONAL CONFERENCE ON WEB RESEARCH. SID. https://sid.ir/paper/1147378/en

Vancouver: Copy

Saed Sajjad, Teimourpour Babak, Kalashi Kamand, Soltanshahi Mohammad Ali. An Efficient Multiple Convolutional Neural Network Model (MCNN-14) for Fashion Image Classification. 2024. Available from: https://sid.ir/paper/1147378/en

IEEE: Copy

Sajjad Saed, Babak Teimourpour, Kamand Kalashi, and Mohammad Ali Soltanshahi, “An Efficient Multiple Convolutional Neural Network Model (MCNN-14) for Fashion Image Classification,” presented at the INTERNATIONAL CONFERENCE ON WEB RESEARCH. 2024, [Online]. Available: https://sid.ir/paper/1147378/en

Related Journal Papers

No record.

Related Seminar Papers

No record.

Related Plans

No record.

Recommended Workshops