US 2009/0290802 A1 · CONCURRENT MULTIPLE-INSTANCE LEARNING FOR IMAGE CATEGORIZATION

Abstract

The concurrent multiple instance learning technique described encodes the inter-dependency between instances (e.g. regions in an image) in order to predict a label for a future instance, and, if desired the label for an image determined from the label of these instances. The technique, in one embodiment, uses a concurrent tensor to model the semantic linkage between instances in a set of images. Based on the concurrent tensor, rank-1 supersymmetric non-negative tensor factorization (SNTF) can be applied to estimate the probability of each instance being relevant to a target category. In one embodiment, the technique formulates the label prediction processes in a regularization framework, which avoids overfitting, and significantly improves a learning machine's generalization capability, similar to that in SVMs. The technique, in one embodiment, uses Reproducing Kernel Hilbert Space (RKHS) to extend predicted labels to the whole feature space based on the generalized representer theorem.

Claims20

Dependent claims

A computer-implemented process for labeling regions in images, comprising:

inputting training images for which image labels are to be learned, and a set of possible image labels;
modeling interdependencies between regions of the input training images that define each image's inherent semantic properties;
inputting a new image for which labels of regions are sought;
and
obtaining a label for each region in the new image using the modeled interdependencies.

2 more elements

Dependencies are inferred from the claim text.

Concurrent multiple-instance learning for image categorization

Life of this patent

Concurrent multiple-instance learning for image categorization

Life of this patent

Abstract

Claims20