29013572
Semi-supervised Learning in Medical Image Database
This paper presents a novel graph-based algorithm for solving the semi-supervised learning problem. The graph-based algorithm makes use of the recent advances in stochastic graph sampling technqiue and a modeling of the labeling consistency in semi-supervised learning. The quality of the algorithm is empirically evaluated on a synthetic clustering problem. The semi-supervised clustering is also applied to the problem of symptoms classification in medical image database and shows promising results.
222927
Regularized color clustering in medical image database
A regularized color clustering algorithm is proposed to solve the color clustering problem in medical image database. By incorporating both measures of cluster separability and cluster compactness, regularized color clustering allows the automatic extraction of significant color groups with varying populations. Experimental results in different color spaces show that the regularized color clustering gives superior results in extracting significant distinct/abnormal color clusters without significant increases in cluster compactness. Furthermore, results of color clustering in different color spaces show that the LUV color space is more suitable for color clustering. Methods for selecting the regularization constants have also been suggested.
46337154
Minimum cross entropy thresholding
Abstract The threshold selection problem is solved by minimizing the cross entropy between the image and its segmented version. The cross entropy is formulated in a pixel-to-pixel basis between the two images and a computationally attractive algorithm employing the histogram is developed. Without making a priori assumptions about the population distribution, this method provides an unbiased estimate of a binarized version of the image in an information theoretic sense.
5657260
Guided Cluster Discovery with Markov Model
Cluster discovery is an essential part of many data mining applications. While cluster discovery process is mainly unsupervised in nature, it can often be aided by a small amount of labeled data. A probabilistic model on the clustering structure is adopted and a novel unified energy equation for clustering that incorporates both labeled data and unlabeled data is introduced. This formulation is inspired by a force-field model integrating labeling constraint on labeled data and similarity information on unlabeled data for joint estimation. Experimental results show that good clusters can be identified using small amount of labeled data.
35216739
Image Smoothing Using Parametric Relaxation
Abstract A new image-smoothing algorithm which smooths the image while preserving discontinuities is developed. The method poses the smoothing problem as an estimation problem of the maximum a posteriori probability of a pixel having a certain gray level. A prior probability density function is assigned to each pixel from the raw data, which is an estimate of the gray-level uncertainty. The algorithm is an application of the random measure field in image processing. Contextual information from neighbors is used in refining the probability distribution of each pixel, which is a continuous version of the relaxation algorithm. The way of parametrizing the probability functions enables the derivation of an efficient iterative formula for updating the gray-level values.
8850461
Schema-guided wrapper maintenance for web-data extraction
Extracting data from Web pages using wrappers is a fundamental problem arising in a large variety of applications of vast practical interests. There are two main issues relevant to Web-data extraction, namely wrapper generation and wrapper maintenance. In this paper, we propose a novel schema-guided approach to the problem of automatic wrapper maintenance. It is based on the observation that despite various page changes, many important features of the pages are preserved, such as syntactic patterns, annotations, and hyperlinks of the extracted data items. Our approach uses these preserved features to identify the locations of the desired values in the changed pages, and repair wrappers correspondingly by inducing semantic blocks from the HTML tree. Our intensive experiments on real Web sites show that the proposed approach can effectively maintain wrappers to extract desired data with high accuracies.
23059132
Image authentication and integrity verification via content-based watermarks and a public key cryptosystem
A technique using the inherent feature map of the underlying image as the watermark is proposed in this work. First, the binary feature map is extracted as watermark and partitioned into blocks. Secondly, neighboring feature map blocks are blended and encrypted for insertion. On the receiver side, the feature map from the received image is extracted again and compared against the recovered watermark to verify the integrity and authenticity. In addition the capability of detecting geometric transformation, removal of original objects and addition of foreign objects, the proposed scheme is also capable of localizing tampering and detecting cropping without a priori knowledge about the image.
402079
Learning What to Talk about in Descriptive Games
Text generation requires a planning module to select an object of discourse and its properties. This is specially hard in descriptive games, where a computer agent tries to describe some aspects of a game world. We propose to formalize this problem as a Markov Decision Process, in which an optimal message policy can be defined and learned through simulation. Furthermore, we propose back-off policies as a novel and effective technique to fight state dimensionality explosion in this framework.
19930573
Learning Pathological Characteristics from User's Relevance Feedback for Content-Based Mammogram Retrieval
Content-based image retrieval (CBIR) has been proposed to address the problem of image retrieval from medical image databases. Relevance feedback, explaining the user's query concept, can be used to bridge the semantic gap and improve the performance of CBIR systems. This paper proposes a learning method for relevance feedback, which utilizes probabilistic model to generalize the 2-class problem and provide an estimate of probability of class membership. To build the probabilistic model, support vector machine (SVM) is applied to classify the mammograms, and then scale them to the probability of class membership. Experimental results show that the proposed learning method can effectively improve the average precision rate from 40% to 62% through five iterations of relevance feedback rounds
35008704
Image segmentation based on a multiresolution Bayesian framework
A multiresolution Markov random field (MMRF) approach to texture segmentation which makes use of both regional and boundary information is proposed in this work. The algorithm is designed to improve the efficiency and solve two problems: over-segmentation and under-segmentation. At each resolution level, regional information and boundary information between textured regions is extracted to describe each block using the multiresolution Fourier transform (MFT). The image is then modelled as a sequence of Markov random fields (MRFs) of varying resolution and a Gibbs sampling scheme is applied to label the constituent sites at each scale by seeking the configuration with minimal interaction energy. The interaction energy is defined as a function of the regional and boundary information. The constraints of region context, and boundary smoothness and connectivity are also encoded in the interaction energy. Once the algorithm converges at a given scale, the segmentation result is propagated down to the next resolution for further refinement.