6840410
Similarity-Driven Cluster Merging Method for Unsupervised Fuzzy Clustering
In this paper, a similarity-driven cluster merging method is proposed for unsupervised fuzzy clustering. The cluster merging method is used to resolve the problem of cluster validation. Starting with an overspecified number of clusters in the data, pairs of similar clusters are merged based on the proposed similarity-driven cluster merging criterion. The similarity between clusters is calculated by a fuzzy cluster similarity matrix, while an adaptive threshold is used for merging. In addition, a modified generalized objective function is used for prototype-based fuzzy clustering. The function includes the p-norm distance measure as well as principal components of the clusters. The number of the principal components is determined automatically from the data being clustered. The properties of this unsupervised fuzzy clustering algorithm are illustrated by several experiments.
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.
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
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.
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.
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).
46281578
Model-Based Active Object Recognition Using MRF Matching and Sensor Planning
This paper presents an active object recognition algorithm using MAP-MRF matching and sensor planning strategy. The matching between the sensed and model object is based on surface properties. A new measure of surface distinguishability is defined for sensor planning. MAP-MRF framework is used for generating matching label set. A measure of confidence of correct match is determined based on the posterior energy. An active object recognition algorithm is used for determining the next viewpoint of the camera if ambiguity exists in the matching result. The next viewpoint is chosen based the surface with highest distinguishability. Experimental results on images under perfect and imperfect segmentation are presented.
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.
15434765
A Dynamic Sub-vector Weighting Scheme for Image Retrieval with Relevance Feedback
In image retrieval with relevance feedback, feature components obtained from low-level descriptors are often 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 the labelled images is often small and an optimal weighting cannot be achieved in practice because of the singularity of the covariance matrix needed for weighting. To solve this problem, a dynamic sub-vector weighting scheme is proposed in this paper. In this scheme, a multi-dimensional feature vector is partitioned into multiple sub-vectors whose dimensions are dynamically adjusted to match the small number of available labelled images. Because of the lower dimensionality of the sub-vectors, the optimal weighting can be performed based on these sub-vectors, respectively. The multiple similarity scores obtained from sub-vector comparisons are then combined, as the final score, to rank the database images and determine the retrieval result. Experimental results demonstrated better performance of the proposed weighting scheme than some existing weighting schemes.
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.