On the use of Textural Features and Neural Networks for Leaf Recognition

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.

Mostajer Kheirkhah Fateme; Asghari Habibollah; YAZDANI DARAB

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

Journal Paper

Paper Information

Journal: SIGNAL AND DATA PROCESSING Year:2019 | Volume:16 | Issue:1 (39) Page(s): 111-123

Download Full-Text

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

Persian Verion

View:

1,280

Download:

Cites:

Information Journal Paper

Title

On the use of Textural Features and Neural Networks for Leaf Recognition

Author(s)

Mostajer Kheirkhah Fateme | Asghari Habibollah | YAZDANI DARAB | Issue Writer Certificate

Keywords

Species recognitionQ1

GIST feature vectorQ1

Neural networksQ2

Gabor filterQ2

Abstract

for recognizing various types of plants, so automatic image recognition algorithms can extract to classify plant species and apply these features. Fast and accurate recognition of plants can have a significant impact on biodiversity management and increasing the effectiveness of the studies in this regard. These automatic methods have involved the development of recognition techniques and digital image processing pattern. Most of the previous studies on the classification and identification of plant species from leaf images are based on the shape, texture and color features. There were also different methods of data modeling which have been used to leave plant recognition. In this paper, we investigate a novel approach for the recognition of plant species using texture feature GIST to extract general features. In the classification step, Patternnet feed forward neural network algorithm has been applied. Essentially, the GIST feature has been designed to be employed for image classification. In this study, GIST feature vectors are considered as the basis of the leaves’ classification. The GIST descriptor of an image is computed by the first filtering of an image by a filter bank of Gabor filters, and then averaging the responses of filters in each block on a no overlapping grid. For evaluation of our approach, we have applied the algorithm on scan and pseudo-scan images of two famous different datasets Image CLEF2012 and Leaf snap with a high various. The results show that in comparison to some widely used algorithms, our approach outperforms in the case of time and also the accuracy of classification. Substantial results can be achieved when the image of the plants are aligned with one another and when we deal with pseudo scan images. The detection of combinations of leaves that have jagged edges is an important contribution of this study. In many of the previous algorithms, the computational complexity of this detection is high. While by using the GIST feature vector, these types of images are processed simply and precisely (above 90%). precisely (above 90%).

Cites

No record.

References

No record.

Cite

APA: Copy

Mostajer Kheirkhah, Fateme, Asghari, Habibollah, & YAZDANI, DARAB. (2019). On the use of Textural Features and Neural Networks for Leaf Recognition. SIGNAL AND DATA PROCESSING, 16(1 (39) ), 111-123. SID. https://sid.ir/paper/160886/en

Vancouver: Copy

Mostajer Kheirkhah Fateme, Asghari Habibollah, YAZDANI DARAB. On the use of Textural Features and Neural Networks for Leaf Recognition. SIGNAL AND DATA PROCESSING[Internet]. 2019;16(1 (39) ):111-123. Available from: https://sid.ir/paper/160886/en

IEEE: Copy

Fateme Mostajer Kheirkhah, Habibollah Asghari, and DARAB YAZDANI, “On the use of Textural Features and Neural Networks for Leaf Recognition,” SIGNAL AND DATA PROCESSING, vol. 16, no. 1 (39) , pp. 111–123, 2019, [Online]. Available: https://sid.ir/paper/160886/en

Related Journal Papers