62765835
Detecting abnormal regions in colonoscopic images by patch-based classifier ensemble
In this paper, a new method is proposed to detect abnormal regions in colonoscopic images by patch-based classifier ensemble. Through supervised learning from image patches of various sizes, a set of basic SVM classifiers is trained for each size. A diagnostic model can then be constructed based on the ensemble of basic classifiers which is then used to detect abnormal regions in colonoscopic images. The multiple sizes of patches provide multiple level representation of the image content, which can help improve detection results. Several fusion criteria are explored to determine the final output of the ensemble. Experimental results show promising performance of our proposed method.
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
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
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.
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.
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.
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.
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.
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.
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.