Title :
A New Method for Semi-Automatic Image Annotation
Author :
Xuelong, Hu ; Yuhui, Zhang ; Li, Yang
Author_Institution :
Yangzhou Univ., Yangzhou
Abstract :
Semantic-based image retrieval bridges the gap between visual features and human understanding of image in the field of image retrieval. Image annotation is one important technology of image retrieval based on the semantic. This paper proposed one method to realize semi-automatic image annotation with the tool Support Vector Machine (SVM). The image collection was divided into two parts, one for manual annotation and the other for testing. After being classified by SVM, the output was changed into a probability, and K-NN algorithm was applied to get the keywords for unlabeled images. The experiments show that the approach is feasible.
Keywords :
feature extraction; image retrieval; support vector machines; K-NN algorithm; SVM; image collection; semantic-based image retrieval; semi-automatic image annotation; support vector machine; unlabeled image keywords; visual features; Humans; Image classification; Image processing; Image retrieval; Image segmentation; Information retrieval; Instruments; Support vector machine classification; Support vector machines; Testing; Image classification; SVM; image retrieval;
Conference_Titel :
Electronic Measurement and Instruments, 2007. ICEMI '07. 8th International Conference on
Conference_Location :
Xi´an
Print_ISBN :
978-1-4244-1136-8
Electronic_ISBN :
978-1-4244-1136-8
DOI :
10.1109/ICEMI.2007.4350818