Mendeley
TY  _  JOUR
ID  -  13980502191034
TI  -  Automatic Image Annotation by Block Principal Pivoting
JO  -  Nashriyyah -i Muhandisi -i Barq va Muhandisi -i Kampyutar -i Iran
JA  -  ES
LA  -  fa
SN  -  16823745
AU  -  Rikabi H.
AU  -  Soufi N.
AU  -  Sadoghi Yazdi Hadi
AU  -  	Taherinia Amir Hossein
AD  -  دانشگاه فردوسي مشهد
AD  -  دانشگاه فردوسي مشهد
AD  -  Ferdowsi University of Mashhad
AD  -  Ferdowsi Uni Of Mashhad
Y1  -  2019
PY  -  2019
VL  -  1
IS  -  
SP  -  25
EP  -  36
KW  -  Non-negative matrix block principal pivotingK-nearest neighborimage annotation
DO  -  
N2  -  Image annotation systems are responsible for describing the content of the images by assigning tags to them. The purpose of this research is to improve the accuracy and speed of image annotation system. Recently, with the growing of images, the image annotation process is based on the basics of images instead of themselves. One of these new methods is the implementation of the non-negative matrix algorithm (NMF) on the features of the images. In the proposed method, for the first time, in order to increase the speed and efficiency of the7 system, we use a method that called the block principal pivoting for the NMF solution. This method has ability to add online new class of data to its knowledge and knowledge learning in a compact form. Moreover, the ability to train based on received data without having to be re-processed. In the training phase, the matrix of the coefficients and the base of the input images are obtained using the Block Principal Pivoting method. Then, at the test phase for the input image, by extracted features of the image and the coefficients obtained from the training phase, the coefficient of belonging to the test image is calculated to each of the classes of training images. Then, this coefficient while searching among the teaching images for assigning the label to test image increases the accuracy of the algorithm. This search is done by the KNN method on the base of the images. To test the proposed method, we used two databases Corel5K and real animal data (derived from 500px) and, finally, compared with existing methods, which we found in the Corel5K database at a precision of 50.20 and real data was 62.89. Precision have been increased considerably.
UR  - rimag.ir/en/Article/28558
L1  - rimag.ir/en/Article/Download/28558
ER  -  
