Image analysis – Pattern recognition – Classification
Reexamination Certificate
2001-02-27
2004-08-17
Mariam, Daniel (Department: 2623)
Image analysis
Pattern recognition
Classification
C382S190000, C382S227000
Reexamination Certificate
active
06778705
ABSTRACT:
BACKGROUND OF THE INVENTION
1. Field of the Invention
The present invention relates generally to object classification, and more particularly, to classification of objects in image data based on individual opinions from a number of classifiers (models) to derive a consensus opinion.
2. Prior Art
The ultimate goal in the design of any pattern recognition system is to achieve the best possible classification (predictive) performance. This objective traditionally led to the development of different classification schemes for the particular pattern recognition problem to be solved. The results of an experimental assessment of the different designs would then be the basis for choosing one of the classifiers (model selection) as a final solution to the problem. It has been observed in such design studies, that although one of the designs would yield the best performance, the sets of patterns misclassified by the different classifiers would not necessarily overlap.
In view of the prior art, there is a need for a method for the classification of objects in image data, which makes use of this observation to achieve the best possible classification performance.
SUMMARY OF THE INVENTION
Therefore it is an object of the present invention to provide a method for the classification of objects in image data which derives a consensus opinion regarding object classification from individual opinions from a number of classifiers (models).
Accordingly, a method for classification of objects in video image data is provided. The method comprises the steps of: detecting moving objects in the image data; extracting two or more features from each detected moving object in the image data; classifying each moving object for each of the two or more features according to a classification method; and deriving a classification for each moving object based on the classification method for each of the two or more features.
Preferably, the method further comprises the step of filtering out unintended moving objects from the detected moving objects, wherein the filtering step filters out the unintended moving objects according to a detected speed and aspect ratio of each detected moving object.
More preferably, the extracting step comprises extracting at least two of x-gradient, y-gradient, and combined xy-gradient features from each of the detected moving objects, and further comprises the steps of smoothing the image data to reduce the effects of noise and then applying a derivative operator over the image data prior to the extracting step.
The classifying step comprises either using the same classification method for each of the two or more features or using at least two different classification methods for at least two of the two or more features.
Preferably, the classification method comprises a Radial Basis Function Network for training and classifying at least one of the detected moving objects and the classifying step comprises outputting a class label identifying a class to which the detected moving object corresponds to and a probability value indicating the probability with which the unknown pattern belongs to the class for each of the two or more features.
Also provided is an apparatus for classification of objects in video image data. The apparatus comprises: means for detecting moving objects in the image data; means for extracting two or more features from each detected moving object in the image data; means for classifying each moving object for each of the two or more features according to a classification method; and means for deriving a classification for each moving object based on the classification method for each of the two or more features.
Still yet provided are a program storage device readable by machine, tangibly embodying a program of instructions executable by the machine to perform the method steps of the present invention and a computer program product embodied in a computer-readable medium for classification of objects in video image data which comprises computer readable program code means for carrying out the method steps of the present invention.
REFERENCES:
patent: 5699119 (1997-12-01), Chung et al.
patent: 5854856 (1998-12-01), Moura et al.
patent: 5862508 (1999-01-01), Nagaya et al.
patent: 6263088 (2001-07-01), Crabtree et al.
patent: 6606412 (2003-08-01), Echigo et al.
patent: 6678413 (2004-01-01), Liang et al.
patent: 2266638 (1993-03-01), None
Lim, et al. (Moving Object Classification in a Domestic Environment using Quadratic Neural Networks, IEEE, pp. 375-383, 1994.*
Donohoe “Combining segmentation and tracking for the classification of moving objects in video scenes”, IEEE, pp. 533-537, 1988.*
Lipton, et al. “Moving target classification and tracking from real-time video”, IEEE, pp. 8-14, 1998.*
Dobnikar “Tracking and classifying 3D objects from TV pictures a neural network approach”, IEEE, pp. 313-324, 1991.*
Gutta, “Face Recognition Using Hybrid Classifiers”, Pattern Recognition, Pergamon Press Inc., vol. 30, No. 4, Apr. 1997, pp. 539-553.
Le Roux, “An Overview of Moving Object Segmentation in Video Images”, Communications and Signal Processing, IEEE, Aug. 1991, pp. 53-57.
Elgammal A., et al., “Non-parametric Model for Background Subtraction”, European Conference on Computer Vision (ECCV) 2000, Dublin Ireland, Jun. 2000.
Battiti R., et al., “Democracy in Neural Nets: Voting Schems for Classification”, Neural Network, vol. 7, No. 44, pp. 691-707, 1994.
Raja Y., et al., “Segmentation and Tracking Using Color Mixture Models”, Proceedings of the 3rdAsian Conference on Computer Vision, vol. I, pp. 607-614, Hong Kong, China, 1/98.
Kittler J., et al., “Combining Classifiers”, Proceedings of ICPR, vo. II, Track B Pattern Recognition and Signal Analysis, pp. 897-901, Aug. 25-29, 1996.
Gutta Srinivas
Philomin Vasanth
Koninklijke Philips Electronics , N.V.
Mariam Daniel
Thorne Gregory L.
LandOfFree
Classification of objects through model ensembles does not yet have a rating. At this time, there are no reviews or comments for this patent.
If you have personal experience with Classification of objects through model ensembles, we encourage you to share that experience with our LandOfFree.com community. Your opinion is very important and Classification of objects through model ensembles will most certainly appreciate the feedback.
Profile ID: LFUS-PAI-O-3333089