US7039239B2
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Method for image region classification using unsupervised and supervised learning
Assignee: Monument Peak Ventures LLC
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Filing: 2002-02-07
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Granted: 2006-05-02
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Expires: 2022-02-07
Abstract
Abstract A method for classification of image regions by probabilistic merging of a class probability map and a cluster probability map includes the steps of a) extracting one or more features from an input image composed of image pixels; b) performing unsupervised learning based on the extracted features to obtain a cluster probability map of the image pixels; c) performing supervised learning based on the extracted features to obtain a class probability map of the image pixels; and d) combining the cluster probability map from unsupervised learning and the class probability map from supervised learning to generate a modified class probability map to determine the semantic class of the image regions. In one embodiment the extracted features include color and textual features.
Inventors
- 👤 Alexander C. Loui
- 👤 Sanjiv Kumar
Bibliographic Details
Application Number
—
Jurisdiction
US
Data Source
google_patents
Last Synchronized
2026-10-25 00:38
CPC Classifications
G; G06; G06V; G06V20/00; G06F; G06F18/00; G06F18/20; G06F18/23; G06F18/232; G06F18/2321; G06F18/24; G06F18/241; G06F18/25; G06F18/254; G06V10/00; G06V10/40; G06V10/50; G06V10/507
Claims Analysis
Total Claims: 30
Claim 1
Independent Claim
155 words
A method for classification of image regions by probabilistic merging of a class probability map and a cluster probability map, said method comprising the steps of: a) extracting one or more features from an input image composed of image pixels; b) performing unsupervised learning based on the extracted features to obtain a cluster probability map of the image pixels; c) performing supervised learning based on the extracted features to obtain a class probability map of the image pixels; and d) combining the cluster probability map from unsupervised learning and the class probability map from supervised learning to generate a modified class probability map to determine the semantic class of the image regions; wherein the unsupervised learning in step b) comprises the steps of: determining number of clusters in the input image; estimating parameters of a probabilistic model describing the clusters; and assigning each image pixel to one of the clusters according to the probabilistic model.
Claim 2
Independent Claim
155 words
A method for classification of image regions by probabilistic merging of a class probability map and a cluster probability map, said method comprising the steps of: a) extracting one or more features from an input image composed of image pixels; b) performing unsupervised learning based on the extracted features to obtain a cluster probability map of the image pixels; c) performing supervised learning based on the extracted features to obtain a class probability map of the image pixels; and d) combining the cluster probability map from unsupervised learning and the class probability map from supervised learning to generate a modified class probability map to determine the semantic class of the image regions; wherein the unsupervised learning in step b) comprises the steps of: determining number of clusters in the input image; estimating parameters of a probabilistic model describing the clusters; and assigning each image pixel to one of the clusters according to the probabilistic model.
Claim 3
Dependent Claim
16 words
The method as claimed in claim 1 wherein the extracted features include color and textual features.
Claim 4
Dependent Claim
34 words
The method as claimed in claim 1 wherein step a) comprises the step of extracting and computing low-level features selected from the group including color, texture, shapes, and wavelet coefficients from the input image.
Claim 5
Dependent Claim
32 words
The method as claimed in claim 1 wherein step a) comprises the step of detecting and extracting semantic-level features selected from the group including faces, people, and structures from the input image.
Claim 6
Dependent Claim
42 words
A computer program product for classification of image regions by probabilistic merging of a class probability map and a cluster probability map comprising: a computer readable storage medium having a computer program stored thereon for performing the steps of claim 1 .
Claim 7
Independent Claim
180 words
A method for classification of image regions by probabilistic merging of a class probability map and a cluster probability map, said method comprising the steps of: a) extracting one or more features from an input image composed of image pixels; b) performing unsupervised learning based on the extracted features to obtain a cluster probability map of the image pixels; c) performing supervised learning based on the extracted features to obtain a class probability map of the image pixels; and d) combining the cluster probability map from unsupervised learning and the class probability map from supervised learning to generate a modified class probability map to determine the semantic class of the image regions; wherein the supervised learning of step c) comprises the steps of: creating a labeled training set belonging to a particular class; determining a number of components required to learn a density function of a given class with the labeled training set as input; estimating parameters of each density function in a mixture model; and assigning each image pixel to one of the classes according to the mixture model.
Claim 8
Independent Claim
180 words
A method for classification of image regions by probabilistic merging of a class probability map and a cluster probability map, said method comprising the steps of: a) extracting one or more features from an input image composed of image pixels; b) performing unsupervised learning based on the extracted features to obtain a cluster probability map of the image pixels; c) performing supervised learning based on the extracted features to obtain a class probability map of the image pixels; and d) combining the cluster probability map from unsupervised learning and the class probability map from supervised learning to generate a modified class probability map to determine the semantic class of the image regions; wherein the supervised learning of step c) comprises the steps of: creating a labeled training set belonging to a particular class; determining a number of components required to learn a density function of a given class with the labeled training set as input; estimating parameters of each density function in a mixture model; and assigning each image pixel to one of the classes according to the mixture model.
Claim 9
Dependent Claim
16 words
The method as claimed in claim 6 wherein the extracted features include color and textual features.
Claim 10
Dependent Claim
34 words
The method as claimed in claim 6 wherein step a) comprises the step of extracting and computing low-level features selected from the group including color, texture, shapes, and wavelet coefficients from the input image.
Claim 11
Dependent Claim
32 words
The method as claimed in claim 6 wherein step a) comprises the step of detecting and extracting semantic-level features selected from the group including faces, people, and structures from the input image.
Claim 12
Dependent Claim
42 words
A computer program product for classification of image regions by probabilistic merging of a class probability map and a cluster probability map comprising: a computer readable storage medium having a computer program stored thereon for performing the steps of claim 6 .
Claim 13
Independent Claim
182 words
A method for classification of image regions by probabilistic merging of a class probability map and a cluster probability map, said method comprising the steps of: a) extracting one or more features from an input image composed of image pixels; b) performing unsupervised learning based on the extracted features to obtain a cluster probability map of the image pixels; c) performing supervised learning based on the extracted features to obtain a class probability map of the image pixels; and d) combining the cluster probability map from unsupervised learning and the class probability map from supervised learning to generate a modified class probability map to determine the semantic class of the image regions; wherein the unsupervised learning of step b) comprises the steps of: determining a number of clusters in the input image using a Kullback-Leibler (KL) divergence method; estimating mean and covariance parameters of a normally distributed probabilistic model describing the clusters using an Expectation-Maximization (EM) technique; and assigning each image pixel to one of the clusters according to the normally distributed probabilistic model by computing a posterior probability using Bayes rule.
Claim 14
Independent Claim
182 words
A method for classification of image regions by probabilistic merging of a class probability map and a cluster probability map, said method comprising the steps of: a) extracting one or more features from an input image composed of image pixels; b) performing unsupervised learning based on the extracted features to obtain a cluster probability map of the image pixels; c) performing supervised learning based on the extracted features to obtain a class probability map of the image pixels; and d) combining the cluster probability map from unsupervised learning and the class probability map from supervised learning to generate a modified class probability map to determine the semantic class of the image regions; wherein the unsupervised learning of step b) comprises the steps of: determining a number of clusters in the input image using a Kullback-Leibler (KL) divergence method; estimating mean and covariance parameters of a normally distributed probabilistic model describing the clusters using an Expectation-Maximization (EM) technique; and assigning each image pixel to one of the clusters according to the normally distributed probabilistic model by computing a posterior probability using Bayes rule.
Claim 15
Dependent Claim
16 words
The method as claimed in claim 11 wherein the extracted features include color and textual features.
Claim 16
Dependent Claim
34 words
The method as claimed in claim 11 wherein step a) comprises the step of extracting and computing low-level features selected from the group including color, texture, shapes, and wavelet coefficients from the input image.
Claim 17
Dependent Claim
32 words
The method as claimed in claim 11 wherein step a) comprises the step of detecting and extracting semantic-level features selected from the group including faces, people, and structures from the input image.
Claim 18
Dependent Claim
42 words
A computer program product for classification of image regions by probabilistic merging of a class probability map and a cluster probability map comprising: a computer readable storage medium having a computer program stored thereon for performing the steps of claim 11 .
Claim 19
Independent Claim
197 words
A method for classification of image regions by probabilistic merging of a class probability map and a cluster probability map, said method comprising the steps of: a) extracting one or more features from an input image composed of image pixels; b) performing unsupervised learning based on the extracted features to obtain a cluster probability map of the image pixels; c) performing supervised learning based on the extracted features to obtain a class probability map of the image pixels; and d) combining the cluster probability map from unsupervised learning and the class probability map from supervised learning to generate a modified class probability map to determine the semantic class of the image regions; wherein the supervised learning of step c) comprises the steps of: creating a labeled training set belonging to a particular class; determining a number of components required to learn a density function of a given class with the labeled training set as input, using a Kullback-Leibler (KL) divergence method; estimating the mean and covariance parameters of each density function in a Gaussian mixture model using an Expectation-Maximization (EM) technique; and assigning each image pixel to one of the classes according to the Gaussian mixture model.
Claim 20
Independent Claim
197 words
A method for classification of image regions by probabilistic merging of a class probability map and a cluster probability map, said method comprising the steps of: a) extracting one or more features from an input image composed of image pixels; b) performing unsupervised learning based on the extracted features to obtain a cluster probability map of the image pixels; c) performing supervised learning based on the extracted features to obtain a class probability map of the image pixels; and d) combining the cluster probability map from unsupervised learning and the class probability map from supervised learning to generate a modified class probability map to determine the semantic class of the image regions; wherein the supervised learning of step c) comprises the steps of: creating a labeled training set belonging to a particular class; determining a number of components required to learn a density function of a given class with the labeled training set as input, using a Kullback-Leibler (KL) divergence method; estimating the mean and covariance parameters of each density function in a Gaussian mixture model using an Expectation-Maximization (EM) technique; and assigning each image pixel to one of the classes according to the Gaussian mixture model.
Claim 21
Dependent Claim
16 words
The method as claimed in claim 16 wherein the extracted features include color and textual features.
Claim 22
Dependent Claim
34 words
The method as claimed in claim 16 wherein step a) comprises the step of extracting and computing low-level features selected from the group including color, texture, shapes, and wavelet coefficients from the input image.
Claim 23
Dependent Claim
32 words
The method as claimed in claim 16 wherein step a) comprises the step of detecting and extracting semantic-level features selected from the group including faces, people, and structures from the input image.
Claim 24
Dependent Claim
42 words
A computer program product for classification of image regions by probabilistic merging of a class probability map and a cluster probability map comprising: a computer readable storage medium having a computer program stored thereon for performing the steps of claim 16 .
Claim 25
Independent Claim
181 words
A method for classification of image regions by probabilistic merging of a class probability map and a cluster probability map, said method comprising the steps of: a) extracting one or more features from an input image composed of image pixels; b) performing unsupervised learning based on the extracted features to obtain a cluster probability map of the image pixels; c) performing supervised learning based on the extracted features to obtain a class probability map of the image pixels; and d) combining the cluster probability map from unsupervised learning and the class probability map from supervised learning to generate a modified class probability map to determine the semantic class of the image regions; wherein step d) comprises the steps of: maximizing a joint likelihood of class and cluster by computing a class conditional probability using Bayes rule; assigning each of the cluster probability maps to one of the classes according to the class conditional probability; and computing the modified class probability map by weighting each pixel probability of the class probability map by the corresponding pixel probability of the cluster probability map.
Claim 26
Independent Claim
181 words
A method for classification of image regions by probabilistic merging of a class probability map and a cluster probability map, said method comprising the steps of: a) extracting one or more features from an input image composed of image pixels; b) performing unsupervised learning based on the extracted features to obtain a cluster probability map of the image pixels; c) performing supervised learning based on the extracted features to obtain a class probability map of the image pixels; and d) combining the cluster probability map from unsupervised learning and the class probability map from supervised learning to generate a modified class probability map to determine the semantic class of the image regions; wherein step d) comprises the steps of: maximizing a joint likelihood of class and cluster by computing a class conditional probability using Bayes rule; assigning each of the cluster probability maps to one of the classes according to the class conditional probability; and computing the modified class probability map by weighting each pixel probability of the class probability map by the corresponding pixel probability of the cluster probability map.
Claim 27
Dependent Claim
16 words
The method as claimed in claim 21 wherein the extracted features include color and textual features.
Claim 28
Dependent Claim
34 words
The method as claimed in claim 21 wherein step a) comprises the step of extracting and computing low-level features selected from the group including color, texture, shapes, and wavelet coefficients from the input image.
Claim 29
Dependent Claim
32 words
The method as claimed in claim 21 wherein step a) comprises the step of detecting and extracting semantic-level features selected from the group including faces, people, and structures from the input image.
Claim 30
Dependent Claim
42 words
A computer program product for classification of image regions by probabilistic merging of a class probability map and a cluster probability map comprising: a computer readable storage medium having a computer program stored thereon for performing the steps of claim 21 .
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