Image analysis – Learning systems – Trainable classifiers or pattern recognizers
Reexamination Certificate
2007-01-30
2007-01-30
Bali, Vikkram (Department: 2624)
Image analysis
Learning systems
Trainable classifiers or pattern recognizers
C382S224000
Reexamination Certificate
active
09729867
ABSTRACT:
Systems and methods are provided through which a graphic image is classified in terms of being natural versus computer generated, or being a scientific slide presentation versus a comic image. The image is classified by extracting appropriate feature(s) from the image, and using the feature(s) to determine, within a predetermined degree of accuracy, the graphic classification of the image.The classification determination uses a trained model. The trained model is created by using machine learning algorithms such as Neural Networks, Support Vector Machines, and Learning Vector Quantizations.Subsequently, the trained model is used to classify a group of images of unknown classification automatically.
REFERENCES:
patent: 5491627 (1996-02-01), Zhang et al.
patent: 5867593 (1999-02-01), Fukuda et al.
patent: 6396954 (2002-05-01), Kondo
patent: 6421463 (2002-07-01), Poggio et al.
Distinguishing photographs and graphics on the WWW, by Athitsos et al., IEEE 1997.
Classifying images on the web automatically, by Lienhart, IEEE Jun. 2002.
Localizing and segmenting text in images and videos, by Lienhart et al, IEEE 2002.
On the segmentation of test in videos, by Wernicke et al , IEEE 2000.
Vailaya, Aditya et al. “A Bayesian Framework for Semantic Classification of Outdoor Vacation Images”.
Vailaya, Aditiya. “Semantic Classification in Image Databases”. Dissertation, 2000.
Gorkani, Monika M., Rosalind W. Picard. “Texture Orientation for Sorting Photos ‘at a Glance’”. IEEE. 1994. pp. 459-464.
Yiu, Elaine, Frederico Girosi. “Image Classification Using Color Cues and Texture Orientation”. 1996.
Bradshaw, Ben. “Semantic Based Image Retrieval: A Probabilistic Approach”. pp. 167-175.
Kingsbury, Nick. “The Dual-Tree Complex Wavelet Transform: A New Efficient Tool for Image Restoration and Enhancement”.
Athitsos, Vassilis et al. “Distinguishing Photographs and Graphics on the World Wide Web”.
Kohonen, Teuvo et al. “LVQ-PAK: The Learning Vector Quantization Program Package”.
Banham, Mark R., Aggelos K. Katsaggelos. “Digital Image Restoration”. IEEE. 1997. pp. 24-41.
Lienhart, Rainer and Axel Wernicke, “Localizing and Segmenting Text in Images and Videos”. IEEE. vol. 12, No. 4, Apr. 2002. pp. 256-268.
Hartmann Alexander J.
Lienhart Rainer W.
Bali Vikkram
Blakely , Sokoloff, Taylor & Zafman LLP
Intel Corporation
LandOfFree
System and method for classification of images and videos does not yet have a rating. At this time, there are no reviews or comments for this patent.
If you have personal experience with System and method for classification of images and videos, we encourage you to share that experience with our LandOfFree.com community. Your opinion is very important and System and method for classification of images and videos will most certainly appreciate the feedback.
Profile ID: LFUS-PAI-O-3823751