Automatic extraction of vertebra regions from a spinal magnetic resonance (MR) image is normally required as the first step to an intelligent spinal MR image diagnosis system. In this work, we develop a fully automatic vertebra detection and segmentation system, which consists of three stages; namely, AdaBoost-based vertebra detection, detection refinement via robust curve fitting, and vertebra segmentation by an iterative normalized cut algorithm. In order to produce an efficient and effective vertebra detector, a statistical learning approach based on an improved AdaBoost algorithm is proposed. A robust estimation procedure is applied on the detected vertebra locations to fit a spine curve, thus refining the above vertebra detection results. This refinement process involves removing the false detections and recovering the miss-detected vertebrae. Finally, an iterative normalized-cut segmentation algorithm is proposed to segment the precise vertebra regions from the detected vertebra locations. In our implementation, the proposed AdaBoost-based detector is trained from 22 spinal MR volume images. The experimental results show that the proposed vertebra detection and segmentation system can achieve nearly 98% vertebra detection rate and 96% segmentation accuracy on a variety of testing spinal MR images. Our experiments also show the vertebra detection and segmentation accuracies by using the proposed algorithm are superior to those of the previous representative methods. The proposed vertebra detection and segmentation system is proved to be robust and accurate so that it can be used for advanced research and application on spinal MR images.

Learning-Based Vertebra Detection and Iterative Normalized-Cut Segmentation for Spinal MRI

In this paper we propose a system for the analysis of user generated video (UGV). UGV often has a rich camera motion structure that is generated at the time the video is recorded by the person taking the video, i.e., the ?camera person.? We exploit this structure by defining a new concept known as camera view for temporal segmentation of UGV. The segmentation provides a video summary with unique properties that is useful in applications such as video annotation. Camera motion is also a powerful feature for identification of keyframes and regions of interest (ROIs) since it is an indicator of the camera person's interests in the scene and can also attract the viewers' attention. We propose a new location-based saliency map which is generated based on camera motion parameters. This map is combined with other saliency maps generated using features such as color contrast, object motion and face detection to determine the ROIs. In order to evaluate our methods we conducted several user studies. A subjective evaluation indicated that our system produces results that is consistent with viewers' preferences. We also examined the effect of camera motion on human visual attention through an eye tracking experiment. The results showed a high dependency between the distribution of fixation points of the viewers and the direction of camera movement which is consistent with our location-based saliency map.

Camera Motion-Based Analysis of User Generated Video

Image annotation can be formulated as a classification problem. Recently, Adaboost learning with feature selection has been used for creating an accurate ensemble classifier. We propose dynamic Adaboost learning with feature selection based on parallel genetic algorithm for image annotation in MPEG-7 standard. In each iteration of Adaboost learning, genetic algorithm (GA) is used to dynamically generate and optimize a set of feature subsets on which the weak classifiers are constructed, so that an ensemble member is selected. We investigate two methods of GA feature selection: a binary-coded chromosome GA feature selection method used to perform optimal feature subset selection, and a bi-coded chromosome GA feature selection method used to perform optimal-weighted feature subset selection, i.e. simultaneously perform optimal feature subset selection and corresponding optimal weight subset selection. To improve the computational efficiency of our approach, master-slave GA, a parallel program of GA, is implemented. k-nearest neighbor classifier is used as the base classifier. The experiments are performed over 2000 classified Corel images to validate the performance of the approaches.

Dynamic Adaboost learning with feature selection based on parallel genetic algorithm for image annotation

A formwork method selection model based on boosted decision trees in tall building construction

Active SVM-based relevance feedback using multiple classifiers ensemble and features reweighting

Boost learning algorithm, such as AdaBoost, has been widely used in a variety of applications in multimedia and computer vision. Relevance feedback-based image retrieval has been formulated as a classification problem with a small number of training samples. Several machine learning techniques have been applied to this problem recently. In this paper, we propose a novel paired feature AdaBoost learning system for relevance feedback-based image retrieval. To facilitate density estimation in our feature learning method, we propose an ID3-like balance tree quantization method to preserve most discriminative information. By using paired feature combination, we map all training samples obtained in the relevance feedback process onto paired feature spaces and employ the AdaBoost algorithm to select a few feature pairs with best discrimination capabilities in the corresponding paired feature spaces. In the AdaBoost algorithm, we employ Bayesian classification to replace the traditional binary weak classifiers to enhance their classification power, thus producing a stronger classifier. Experimental results on content-based image retrieval (CBIR) show superior performance of the proposed system compared to some previous methods.

Improved AdaBoost-based image retrieval with relevance feedback via paired feature learning

In this paper, we propose a novel paired feature learning system for relevance feedback based image retrieval. To facilitate density estimation in our feature learning system, we employ an ID3-like balance tree quantization method to preserve most discriminative information. In addition, we map all training samples in the relevance feedback onto paired feature spaces to enhance the discrimination power of feature representation. Furthermore, we replace the traditional binary classifiers in the AdaBoost learning algorithm by Bayesian weak classifiers to improve its accuracy, thus producing stronger classifiers. Experimental results on content-based image retrieval show improvement of each step in the proposed learning system.

Improved adaboost-based image retrieval with relevance feedback via paired feature learning

An integrated intelligent home video management system was proposed in this paper. Five different types of multimedia representative features were computed as the basis of the home video management system. With the aid of some machine learning techniques, such as SVM, neural network, adaboost algorithm, and the K-means clustering algorithm, we develop six main applications based on this system. These applications include detection of abnormal camera operation, shot boundary detection, fast-pan detection, face shot identification, key-frame extraction, and variable length video abstraction. Our system can help inexpert digital camcorder users manage their home video effectively in a smart way.

Intelligent home video management system

Given a graph, the Hamiltonian path completion problem is to find an augmenting edge set such that the augmented graph has a Hamiltonian path. In this paper, we show that the Hamiltonian path completion problem will unlikely have any constant ratio approximation algorithm unless NP = P. This problem remains hard to approximate even when the given subgraph is a tree. Moreover, if the edge weights are restricted to be either 1 or 2, the Hamiltonian path completion problem on a tree is still NP-hard. Then it is shown that this problem will unlikely have any fully polynomial-time approximation scheme (FPTAS) unless NP=P. When the given tree is a k-tree, we give an approximation algorithm with performance ratio 1.5.

Qi-Jiunn Wu

Papers

Improved AdaBoost-based image retrieval with relevance feedback via paired feature learning

Improved adaboost-based image retrieval with relevance feedback via paired feature learning

Intelligent home video management system

An Approximate Algorithm for the Weighted Hamiltonian Path Completion Problem on a Tree