2065910
Bayesian Inference on Principal Component Analysis Using Reversible Jump Markov Chain Monte Carlo
Based on the probabilistic reformulation of principal component analysis (PCA), we consider the problem of determining the number of principal components as a model selection problem. We present a hierarchical model for probabilistic PCA and construct a Bayesian inference method for this model using reversible jump Markov chain Monte Carlo (MCMC). By regarding each principal component as a point in a one-dimensional space and employing only birth-death moves in our reversible jump methodology, our proposed method is simple and capable of automatically determining the number of principal components and estimating the parameters simultaneously under the same disciplined framework. Simulation experiments are performed to demonstrate the effectiveness of our MCMC method.
5498052
Natural image retrieval based on features of homogeneous color regions
Natural image retrieval using low-level visual features is a challenging problem for content-based image retrieval. In this paper, a region-based image retrieval (RBIR) approach is proposed. Each image is represented by several feature vectors extracted from homogeneous color regions within an image, and similar images are retrieved based on these region features. In the experimental image database all images are grouped into 16 categories using a moment feature to speed up the retrieval performance. Color mean, color histogram and moment of regions are used as features. From the experiments, it is found that region-based retrieval returns more relevant images than using features based on the entire image.
6480622
Mapping low-level features to high-level semantic concepts in region-based image retrieval
In this paper, a novel offline supervised learning method is proposed to map low-level visual features to high-level semantic concepts for region-based image retrieval. The contributions of this paper lie in three folds. (1) For each semantic concept, a set of low-level tokens are extracted from the segmented regions of training images. Those tokens capture the representative information for describing the semantic meaning of that concept; (2) a set of posteriors are generated based on the low-level tokens through pairwise classification, which denote the probabilities of images belonging to the semantic concepts. The posteriors are treated as high-level features that connect images with high-level semantic concepts. Long-term relevance feedback learning is incorporated to provide the supervisory information needed in the above offline learning process, including the concept information and the relevant training set for each concept; (3) an integrated algorithm is implemented to combine two kinds of information for retrieval: the information from the offline feature-to-concept mapping process and the high-level semantic information from the long-term learned memory. Experimental evaluation on 10,000 images proves the effectiveness of our method.
6521345
Using Statistical Shape Priors in Geodesic Active Contours for Robust Object Detection
A novel statistical shape prior model based on level set representations is proposed in this paper for robust object detection by geodesic active contours. This prior model is able to accommodate multiple shape states of objects. The level set representations (signed distance map) of the shapes are considered to form distinct clusters in a low dimensional feature subspace and a Gaussian mixture model (GMM) is employed to fit the feature distribution in the subspace. A Bayesian classifier is used to assign the currently detected object to the most similar shape cluster. A shape prior is then constructed from the statistical properties of that cluster and is used to drive the geodesic active contour curve towards it in the subsequent evolution. Experiments demonstrate the effectiveness of our shape prior model
272438
Hybrid Kernel Machine Ensemble for Imbalanced Data Sets
A two-class imbalanced data problem (IDP) emerges when the data from majority class are compactly clustered and the data from minority class are scattered. Though a discriminative binary support vector machine (SVM) can be trained by manually balancing the data, its performance is usually poor due to the inadequate representation of the minority class. A recognition-based one-class SVM can be trained using the data from the well-represented class only. However, it is not highly discriminative. Exploiting the complementary natures of the two types of SVMs in an ensemble can bring benefits from both worlds in addressing the IDP. Experimental results on both artificial and real benchmark data sets support the feasibility of our proposed method
31213439
Nasopharyngeal carcinoma lesion segmentation from MR images by support vector machine
A two-class support vector machine (SVM)-based image segmentation approach has been developed for the extraction of nasopharyngeal carcinoma (NPC) lesion from magnetic resonance (MR) images. By exploring two-class SVM, the developed method can learn the actual distribution of image data without prior knowledge and draw an optimal hyperplane for class separation, via an SVM parameters training procedure and an implicit kernel mapping. After learning, segmentation task is performed by the trained SVM classifier. The proposed technique is evaluated by 39 MR images with NPC and the results suggest that the proposed query-based approach provides an effective method for NPC extraction from MR images with high accuracy
5317364
A criterion for optimizing kernel parameters in KBDA for image retrieval
A criterion is proposed to optimize the kernel parameters in Kernel-based Biased Discriminant Analysis (KBDA) for image retrieval. Kernel parameter optimization is performed by optimizing the kernel space such that the positive images are well clustered while the negative ones are pushed far away from the positives. The proposed criterion measures the goodness of a kernel space, and the optimal kernel parameter set is obtained by maximizing this criterion. Retrieval experiments on two benchmark image databases demonstrate the effectiveness of proposed criterion for KBDA to achieve the best possible performance at the cost of a small fractional computational overhead.
38163613
Bayesian Learning for Image Retrieval Using Multiple Features
Image retrieval using multiple features often uses explicit weights that represent the importance of the features in their similarity metrics. In this paper, a novel retrieval method based on Bayesian Learning is presented. Instead of giving every feature a weight explicitly, the importance of a feature is regulated implicitly by learning a user's perception. Thus, the process of feature combination is adaptive and approximate to a user's perception. Experimental results demonstrate the significance of this method for improving the retrieval efficiency.
34632260
Restricted Random Testing
This paper presents a novel adaptation of traditional random testing, called Restricted Random Testing (RRT). RRT offers a significant improvement over random testing, as measured by the F-measure. This paper describes the ideology behind RRT and explains its algorithm. RRT's performance is examined using several experiments, the results of which are presented and discussed.
17672081
A Sub-Vector Weighting Scheme for Image Retrieval with Relevance Feedback
In image retrieval with relevance feedback, feature components are weighted to reflect the high-level concepts, and a user's subjective perception, embodied in the images labelled by the user in the feedback. However, the number of labelled images is often small and the covariance matrix needed for weighting will be singular. For this reason, the commonly used methods discard the mutual correlation among the feature components completely and use a diagonal covariance matrix. In this paper, a sub-vector weighting scheme is proposed. This scheme partitions a multi-dimensional visual feature vector into multiple low-dimensional sub-vectors. The singularity of the covariance matrix for each sub-vector can be avoided due to the lower dimensionality of the sub-vectors. Thus, the mutual correlation in each sub-vector can be retained for weighting and an optimally weighted similarity metric can be applied on each sub-vector. The similarity scores obtained from different sub-vectors are combined, as the final scor...
33125229
Content-Based Image Retrieval Using Regional Representation
Representing general images using global features extracted from the entire image may be inappropriate because the images often contain several objects or regions that are totally different from each other in terms of visual image properties. These features cannot adequately represent the variations and hence fail to describe the image content correctly. We advocate the use of features extracted from image regions and represent the images by a set of regional features. In our work, an image is segmented into homogeneous regions using a histogram clustering algorithm. Each image is then represented by a set of regions with region descriptors. Region descriptors consist of feature vectors representing color, texture, area and location of regions. Image similarity is measured by a newly proposed Region Match Distance metric for comparing images by region similarity. Comparison of image retrieval using global and regional features is presented and the advantage of using regional representation is demonstrated.