In this paper, we exploit the context information embodied in an image to develop a machine learning method called context ranking machine (CRM). Specifically, we leverage two kinds of context information: identity context and metric context. The identity context of an image patch refers to its origin (e.g., from which image it is cropped), and the metric context refers to its distance to the exact surrounding box of the target object inside the image. We use these context information in two ways. First, for object localization, instead of learning classifiers to separate the whole negative pool from all positives, we separate each positive from its own negatives sharing the same identity context. Second, we rank image patches according to their resemblance to the ground truth by establishing a connection between appearance based features and metric properties of the image. The CRM learns an image-based ranking algorithm via boosting and achieves an improved localization accuracy. We performed tests on echocardiogram images to localize heart chambers, and face images for eye band localization.

We propose a fully automatic system for cardiac view classification of echocardiogram. Given an echo study video sequence, the system outputs a view label among the pre-defined standard views. The system is built based on a machine learning approach that extracts knowledge from an annotated database. It characterizes three features: 1) integrating local and global evidence, 2) utilizing view specific knowledge, and 3) employing a multi-class Logit-boost algorithm. In our prototype system, we classify four standard cardiac views: apical four chamber and apical two chamber, parasternal long axis and parasternal short axis (at mid cavity). We achieve a classification accuracy over 96% both of training and test data sets and the system runs in a second in the environment of Pentium 4 PC with 3.4 GHz CPU and 1.5 G RAM.

We tackle the problem of automatically classifying cardiac view for an echocardiographic sequence as a multiclass object detection. As a solution, we present an imagebased multiclass boosting procedure. In contrast with conventional approaches for multiple object detection that train multiple binary classifiers, one per object, we learn only one multiclass classifier using the LogitBoosting algorithm. To utilize the fact that, in the midst of boosting, one class is fully separated from the remaining classes, we propose to learn a tree structure that focuses on the remaining classes to improve learning efficiency. Further, we accommodate the large number of background images using a cascade of boosted multiclass classifiers, which is able to simultaneously detect and classify multiple objects while rejecting the background class quickly. Our experiments on echocardiographic view classification demonstrate promising performances of image-based multiclass boosting.

A watermarking scheme is presented which embeds two different watermarks to the same frequency and extracts the marks with two different methods in the frequency domain. One of the two extraction methods needs a source image, whereas the other does not. Each extraction method uses a unique operation in extracting the watermark. The use of two watermark schemes is more effective in claiming rightful ownership. Furthermore, this watermarking is non-invertible and is robust against IBM attack as it obtains two different extraction results from the same image.