Title :
Clothes Image Searching System Based on SIFT Features
Author_Institution :
Coll. of Comput. & Inf. Eng., Zhejiang Gongshang Univ., Hangzhou
Abstract :
One of the main difficulties in Clothes image searching is the variability of appearance of an object of interest. Clothes image feature extraction is a very active research area under this situation. Through build a Clothes concept semantic structure Clothes Tree Semantic Structure, the Clothes Image Searching System based on Scale Invariant Feature Transform(SIFT) is proposed in the paper. Experiment on an image database of about 5,000 general-purpose images, results show that our algorithm is more efficient than existing algorithms based on color histogram or corners features.
Keywords :
feature extraction; image retrieval; query formulation; transforms; SIFT features; clothes image feature extraction; clothes image searching system; clothes tree semantic structure; concept semantic structure; scale invariant feature transform; Cascading style sheets; Clustering algorithms; Educational institutions; Feature extraction; Histograms; Image databases; Image retrieval; Image segmentation; Information retrieval; Spatial databases;
Conference_Titel :
E-Business and Information System Security, 2009. EBISS '09. International Conference on
Conference_Location :
Wuhan
Print_ISBN :
978-1-4244-2909-7
Electronic_ISBN :
978-1-4244-2910-3
DOI :
10.1109/EBISS.2009.5137951
