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
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.
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.
62767012
Incorporating prior knowledge into SVM for image retrieval
SVM based image retrieval suffers from the scarcity of labelled samples. In this paper, this problem is solved by incorporating prior knowledge into SVM. Firstly, some prior knowledge of image retrieval is discussed and constructed. After that, the knowledge is incorporated into SVM optimization as a constraint, and a new knowledge-based target function is formulated. Based on this, a framework of image retrieval with knowledge based SVM is proposed. Experimental results demonstrate that the proposed method can effectively improve the learning and retrieval performance of SVM, especially when the number of labelled samples is small.
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
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
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.
18212897
Performance Evaluation of the Nearest Feature Line Method in Image Classification and Retrieval
A method, the nearest feature line (NFL) method, is used in image classification and retrieval and its performance is evaluated and compared with other methods by extensive experiments. The NFL method is demonstrated to make efficient use of knowledge about multiple prototypes of a class to represent that class.
45421467
Support vector machines for face recognition
Abstract Support vector machines (SVMs) have been recently proposed as a new learning network for bipartite pattern recognition. In this paper, SVMs incorporated with a binary tree recognition strategy are proposed to tackle the multi-class face recognition problem. The binary tree extends naturally, the pairwise discrimination capability of the SVMs to the multi-class scenario. Two face databases are used to evaluate the proposed method. The performance of the SVMs based face recognition is compared with the standard eigenface approach, and also the more recently proposed algorithm called the nearest feature line (NFL).
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...
4472556
Variational Bayesian Learning of ICA with Missing Data
Missing data are common in real-world data sets and are a problem for many estimation techniques. We have developed a variational Bayesian method to perform independent component analysis (ICA) on high-dimensional data containing missing entries. Missing data are handled naturally in the Bayesian framework by integrating the generative density model. Modeling the distributions of the independent sources with mixture of gaussians allows sources to be estimated with different kurtosis and skewness. Unlike the maximum likelihood approach, the variational Bayesian method automatically determines the dimensionality of the data and yields an accurate density model for the observed data without overfitting problems. The technique is also extended to the clusters of ICA and supervised classification framework.