公开(公告)号：GB2496009A

公开(公告)日：2013-05-01

申请号：GB201213099

申请日：2012-07-24

Applicant: IBM

Inventor： LI YING ,  PALURI BALAMANOHAR ,  OTTO CHARLES ,  PANKANTI SHARATHCHANDRA UMAPATHIRAO ,  FUJIKI YUICHI ,  HAAS NORMAN

IPC: G06T7/00

Abstract: A system, method, and computer program product are described for detecting anomalies in an image. In an example embodiment the method includes partitioning 304 each image of a set of images into a plurality of image local units. The method further includes clustering 308 all local units in the image set into clusters, and consequently assigning 310 a class label to each local unit based on the clustering results, wherein the local units with identical class labels have at least one substantially related image feature. Further, the method includes assigning 312 a weight to each of the local units based on a variation of the class labels across all images in a set of images. The method further includes performing a clustering over all images in the set, for example by using a distance metric that takes the learned weight of each local unit into account, then determining the images that belong to minorities of the clusters as anomalous.

公开(公告)号：GB2496009B

公开(公告)日：2015-03-04

申请号：GB201213099

申请日：2012-07-24

Applicant: IBM

Inventor： LI YING ,  PALURI BALAMANOHAR ,  OTTO CHARLES ,  PANKANTI SHARATHCHANDRA UMAPATHIRAO ,  FUJIKI YUICHI ,  HAAS NORMAN

IPC: G06K9/62 ,  G06T7/00

Abstract: A system, method, and computer program product for detecting anomalies in an image. In an example embodiment the method includes partitioning each image of a set of images into a plurality of image local units. The method further includes clustering all local units in the image set into clusters, and consequently assigning a class label to each local unit based on the clustering results. The local units with identical class labels having at least one substantially related image feature. Further, the method includes assigning a weight to each of the local units based on a variation of the class labels across all images in a set of images. The method further includes performing a clustering over all images in the set by using a distance metric that takes the learned weight of each local unit into account, then determining the images that belong to minorities of the clusters as anomalies.