Method and system for fuzzy clustering of images
First Claim
1. A method for clustering a set of N images into P final clusters, where N and P are integers, the method comprising the steps of:
- (a) calculating at least one similarity measure Sij between members of each possible pair of images, wherein Sij represents the similarity measure between the ith and jth images with i and j being image indices and Sij=Sj,i;
(b) calculating a total connectivity value for each of the images remaining to be clustered, a total connectivity value for each image being defined as a sum of a function f of the similarity measures associated with that image;
(c) identifying, from among said images remaining to be clustered, a maximum total connectivity value Tmax corresponding to an image Imax, image Imax belonging to a current cluster C which initially includes all images remaining to be clustered;
(d) removing, from the current cluster C, at least one image based on at least one of its similarity measure with image Imax and its total connectivity value within current cluster C;
(e) adding, to current cluster C, images having a similarity measure that is greater than a threshold T3 with any image currently in C;
(f) calculating, for each image within current cluster C, a total connectivity value based on those images within C;
(g) removing, from current cluster C, those images having a total connectivity value less than some threshold T4;
(h) repeating steps (f) and (g) until no further images are removed to thereby establish current cluster C as one of the final clusters;
(i) removing all images in current cluster C from further consideration; and
(j) repeating steps (b)-(i) until all N images are assigned to a final cluster.
1 Assignment
0 Petitions
Accused Products
Abstract
An approach to clustering a set of images based on similarity measures employs a fuzzy clustering paradigm in which each image is represented by a node in a graph. The graph is ultimately partitioned into subgraphs, each of which represent true clusters among which the various images are distributed. The partitioning is performed in a series of stages by identifying one true cluster at each stage, and removing the nodes belonging to each identified true cluster from further consideration so that the remaining, unclustered nodes may then be grouped. At the beginning of each such stage, the nodes that remain to be clustered are treated as all belonging to a single candidate cluster. Nodes are removed from this single candidate cluster in accordance with similarity and connectivity criteria, to arrive at a true cluster. The member nodes of this true cluster are then removed from further consideration, prior to the next stage in the process.
27 Citations
17 Claims
-
1. A method for clustering a set of N images into P final clusters, where N and P are integers, the method comprising the steps of:
-
(a) calculating at least one similarity measure Sij between members of each possible pair of images, wherein Sij represents the similarity measure between the ith and jth images with i and j being image indices and Sij=Sj,i;
(b) calculating a total connectivity value for each of the images remaining to be clustered, a total connectivity value for each image being defined as a sum of a function f of the similarity measures associated with that image;
(c) identifying, from among said images remaining to be clustered, a maximum total connectivity value Tmax corresponding to an image Imax, image Imax belonging to a current cluster C which initially includes all images remaining to be clustered;
(d) removing, from the current cluster C, at least one image based on at least one of its similarity measure with image Imax and its total connectivity value within current cluster C;
(e) adding, to current cluster C, images having a similarity measure that is greater than a threshold T3 with any image currently in C;
(f) calculating, for each image within current cluster C, a total connectivity value based on those images within C;
(g) removing, from current cluster C, those images having a total connectivity value less than some threshold T4;
(h) repeating steps (f) and (g) until no further images are removed to thereby establish current cluster C as one of the final clusters;
(i) removing all images in current cluster C from further consideration; and
(j) repeating steps (b)-(i) until all N images are assigned to a final cluster. - View Dependent Claims (2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16)
-
-
17. A method for clustering a set of N items into P final clusters, where N and P are integers, the method comprising the steps of:
-
(a) calculating at least one similarity measure Sij between members of each possible pair of items, wherein Sij represents the similarity measure between the ith and jth items with i and j being item indices and Sij=Sj,i;
(b) calculating a total connectivity value for each of the items remaining to be clustered, a total connectivity value for each item being defined as a sum of a function f of the similarity measures associated with that item;
(c) identifying, from among said items remaining to be clustered, a maximum total connectivity value tmax corresponding to an item Imax, item Imax belonging to a current cluster C which initially includes all items remaining to be clustered;
(d) removing, from the current cluster C, at least one item based on at least one of its similarity measure with item Imax and its total connectivity value within current cluster C;
(e) adding, to current cluster C, items having a similarity measure that is greater than a threshold T3 with any item currently in C;
(f) calculating, for each item within current cluster C, a total connectivity value based on those items within C;
(g) removing, from current cluster C, those items having a total connectivity value less than some threshold T4;
(h) repeating steps (f) and (g) until no further items are removed to thereby establish current cluster C as one of the final clusters;
(i) removing all items in current cluster C from further consideration; and
(j) repeating steps (b)—
(i) until all N items are assigned to a final cluster.
-
Specification