Title :
Saliency based joint topic discovery for object categorization
Author :
Li, Zhidong ; Wang, Yang ; Geers, Glenn ; Chen, Jing ; Yang, Jun ; Laird, John
Abstract :
We present a novel approach of saliency based image categorization using topic model. In each image, salient foreground objects are discriminated from background scene by saliency detection. Then topic model is used to jointly discover topics of foreground and background. Our approach can categorize images in a completely unsupervised manner and achieve higher performance than previous categorization methods, especially for those images with similar foreground/background.
Keywords :
image retrieval; unsupervised learning; background scene; object categorization; saliency based image categorization; saliency based joint topic discovery; saliency detection; salient foreground object; topic model; unsupervised learning; Accuracy; Computational modeling; Computer vision; Conferences; Image segmentation; Probabilistic logic; Visualization; PLSA; image categorization; saliency detection; topic model; unsupervised learning;
Conference_Titel :
Image Processing (ICIP), 2010 17th IEEE International Conference on
Conference_Location :
Hong Kong
Print_ISBN :
978-1-4244-7992-4
Electronic_ISBN :
1522-4880
DOI :
10.1109/ICIP.2010.5652167
