Face annotation technology is important for a photo management system. In this paper, we propose a novel interactive face annotation framework combining unsupervised and interactive learning. There are two main contributions in our framework. In the unsupervised stage, a partial clustering algorithm is proposed to find the most evident clusters instead of grouping all instances into clusters, which leads to a good initial labeling for later user interaction. In the interactive stage, an efficient labeling procedure based on minimization of both global system uncertainty and estimated number of user operations is proposed to reduce user interaction as much as possible. Experimental results show that the proposed annotation framework can significantly reduce the face annotation workload and is superior to existing solutions in the literature.

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A Face Annotation Framework with Partial Clustering and Interactive Labeling

A multi-stage process detects and identifies radiation, explosives, and special materials within a shipping container. The process utilizes radiation sensors configured as nodes on a distributed network. The process collects radiation data from the nodes. The radiation data is associated with the container and its contents. The collected radiation data is dynamically adjusted according to dynamically changing background radiation data, such as relating to water, land, air, ground, and other structures. The process compares collected and adjusted radiation data to spectral images representing isotopes to identify one or more isotopes present. Identified isotopes are corresponded to possible materials that they represent. The possible materials are compared with the manifest of the container to confirm the identity of materials contained in the container or to detect and/or identify unauthorized materials in the container. A neutron pulse device could be used to identify shielded materials, explosives, and other types of materials.

Multi-stage system for verification of container contents

The described implementations relate to assisted face recognition tagging of digital images, and specifically to context-driven assisted face recognition tagging. In one case, context-driven assisted face recognition tagging (CDAFRT) tools can access face images associated with a photo gallery. The CDAFRT tools can perform context-driven face recognition to identify individual face images at a specified probability. In such a configuration, the probability that the individual face images are correctly identified can be higher than attempting to identify individual face images in isolation.

Assisted face recognition tagging

Representative embodiments are directed to systems and methods for processing training data for a statistical classification application. In one embodiment, confidence values are calculated for training data elements to identify the probabilities of the training data elements belonging to identified classes (303). An interactive scatter plot is generated using the calculated confidence values (304). The scatter plot visually indicates the confidence values of points in the scatter plot. Accordingly, the user is able to identify potentially misclassified training data elements. The user may select training data elements from the scatter plot and reclassify training elements as appropriate (314). Upon reclassification, the confidence values may be recalculated and the scatter plot revised.

System and method for processing training data for a statistical application

Methods and apparatus for processing features sampled and stored in a computing system are disclosed. Pattern recognition techniques are disclosed that facilitate decision making functions in computing systems, such as, for example, vehicle occupant safety systems and data mining applications. The disclosed correlation processing methods and apparatus improve the accuracy of data pattern recognition systems, including image processing systems.

Pattern recognition method and apparatus for feature selection and object classification

Stand-alone or assistive pattern recognition system and process enabling error free classification of all objects in a training set and application to unclassified objects. Parameters and/or features of the data objects in a training set are selected and measured, from which discriminants are computed. The measured data is plotted in discriminant space and decision boundaries or thresholds determined, preferably such that at least one object from one class is isolated from the remaining objects, removed from the training set, and the process repeated until an acceptable number of unclassified objects remain. The system can be applied sequentially to classify all the members of the training set belonging to one class and then applied to objects in other classes. Fuzzy quantifiable determinations of an object's likelihood of class membership can be made. Objects' positions and classifications are obtainable in an optical system using Fourier techniques without limitation to linearly discriminable problems.

H. John Caulfield

Papers

Method and system for improving pattern recognition system performance