Objective methods for assessing perceptual image quality traditionally attempted to quantify the visibility of errors (differences) between a distorted image and a reference image using a variety of known properties of the human visual system. Under the assumption that human visual perception is highly adapted for extracting structural information from a scene, we introduce an alternative complementary framework for quality assessment based on the degradation of structural information. As a specific example of this concept, we develop a structural similarity index and demonstrate its promise through a set of intuitive examples, as well as comparison to both subjective ratings and state-of-the-art objective methods on a database of images compressed with JPEG and JPEG2000. A MATLAB implementation of the proposed algorithm is available online at http://www.cns.nyu.edu//spl sim/lcv/ssim/.

/pdf/image-quality-assessment-from-error-visibility-to-structural-1rlwcqe34t.pdf

Image quality assessment: from error visibility to structural similarity

다중혈관 관상동맥 환자에서 y-문합을 이용하여 양쪽 내흉동맥만을 사용한 우회술의 조기 성적

Machine Learning is the study of methods for programming computers to learn. Computers are applied to a wide range of tasks, and for most of these it is relatively easy for programmers to design and implement the necessary software. However, there are many tasks for which this is difficult or impossible. These can be divided into four general categories. First, there are problems for which there exist no human experts. For example, in modern automated manufacturing facilities, there is a need to predict machine failures before they occur by analyzing sensor readings. Because the machines are new, there are no human experts who can be interviewed by a programmer to provide the knowledge necessary to build a computer system. A machine learning system can study recorded data and subsequent machine failures and learn prediction rules. Second, there are problems where human experts exist, but where they are unable to explain their expertise. This is the case in many perceptual tasks, such as speech recognition, hand-writing recognition, and natural language understanding. Virtually all humans exhibit expert-level abilities on these tasks, but none of them can describe the detailed steps that they follow as they perform them. Fortunately, humans can provide machines with examples of the inputs and correct outputs for these tasks, so machine learning algorithms can learn to map the inputs to the outputs. Third, there are problems where phenomena are changing rapidly. In finance, for example, people would like to predict the future behavior of the stock market, of consumer purchases, or of exchange rates. These behaviors change frequently, so that even if a programmer could construct a good predictive computer program, it would need to be rewritten frequently. A learning program can relieve the programmer of this burden by constantly modifying and tuning a set of learned prediction rules. Fourth, there are applications that need to be customized for each computer user separately. Consider, for example, a program to filter unwanted electronic mail messages. Different users will need different filters. It is unreasonable to expect each user to program his or her own rules, and it is infeasible to provide every user with a software engineer to keep the rules up-to-date. A machine learning system can learn which mail messages the user rejects and maintain the filtering rules automatically. Machine learning addresses many of the same research questions as the fields of statistics, data mining, and psychology, but with differences of emphasis. Statistics focuses on understanding the phenomena that have generated the data, often with the goal of testing different hypotheses about those phenomena. Data mining seeks to find patterns in the data that are understandable by people. Psychological studies of human learning aspire to understand the mechanisms underlying the various learning behaviors exhibited by people (concept learning, skill acquisition, strategy change, etc.).

Machine learning

A general non-parametric technique is proposed for the analysis of a complex multimodal feature space and to delineate arbitrarily shaped clusters in it. The basic computational module of the technique is an old pattern recognition procedure: the mean shift. For discrete data, we prove the convergence of a recursive mean shift procedure to the nearest stationary point of the underlying density function and, thus, its utility in detecting the modes of the density. The relation of the mean shift procedure to the Nadaraya-Watson estimator from kernel regression and the robust M-estimators; of location is also established. Algorithms for two low-level vision tasks discontinuity-preserving smoothing and image segmentation - are described as applications. In these algorithms, the only user-set parameter is the resolution of the analysis, and either gray-level or color images are accepted as input. Extensive experimental results illustrate their excellent performance.

Mean shift: a robust approach toward feature space analysis

Probability Distributions.- Linear Models for Regression.- Linear Models for Classification.- Neural Networks.- Kernel Methods.- Sparse Kernel Machines.- Graphical Models.- Mixture Models and EM.- Approximate Inference.- Sampling Methods.- Continuous Latent Variables.- Sequential Data.- Combining Models.

Pattern Recognition and Machine Learning

Motivation: Missing data in genotyping single nucleotide polymorphism (SNP) spots are common. High-throughput genotyping methods usually have a high rate of missing data. For example, the published human chromosome 21 data by Patil et al. contains about 20% missing SNPs. Inferring missing SNPs using the haplotype block structure is promising but difficult because the haplotype block boundaries are not well defined. Here we propose a global algorithm to overcome this difficulty.

Results: First, we propose to use entropy as a measure of haplotype diversity. We show that the entropy measure combined with a dynamic programming algorithm produces better haplotype block partitions than other measures. Second, based on the entropy measure, we propose a two-step iterative partition-inference algorithm for the inference of missing SNPs. At the first step, we apply the dynamic programming algorithm to partition haplotypes into blocks. At the second step, we use an iterative process similar to the expectation-maximization algorithm to infer missing SNPs in each haplotype block so as to minimize the block entropy. The algorithm iterates these two steps until the total block entropy is minimized. We test our algorithm in several experimental data sets. The results show that the global approach significantly improves the accuracy of the inference.

Availability: Upon request.

Contact: tingchen@usc.edu

Inference of missing SNPs and information quantity measurements for haplotype blocks

A non-collaborative coexistence mechanism for wireless-fidelity (Wi-Fi) and bluetooth (BT) systems based on dynamic packet fragmentation is proposed in this work. The basic idea is to adapt the packet length of Wi-Fi in the MAC layer such that the fragmented packet has a better chance to survive the interference from the nearby BT devices. We first develop an analytical model that specifies the information required by the Wi-Fi MAC layer to decide the best fragmentation strategy. Then, this model is extended to analyze the throughput and transmission delay of the Wi-Fi device. The analytical model is validated by computer simulation. Furthermore, it is demonstrated by simulation results that the proposed coexistence mechanism improves the performance of Wi-Fi in throughput and transmission delay significantly while relatively smaller performance improvement is observed for BT.

/pdf/coexistence-wi-fi-mac-design-for-mitigating-interference-27y776ugnu.pdf

Coexistence Wi-Fi MAC Design for Mitigating Interference Caused by Collocated Bluetooth

The structure analysis of pop and rock songs from audio signals is conducted via similarity matrix processing in this work. The similarity matrix offers pairwise similarity between any two short intervals of fixed length in a song. We use two similarity matrices to show their diverse characteristics. The characteristics are explained by musical chord successions. Then, several similarity matrix processing techniques are developed for music structure analysis. First, an algorithm is proposed to check the boundaries and periods of repetitive chord successions with short periods. Second, the Viterbi algorithm is applied to detect straight segments in sub-diagonal lines of the similarity matrix. Periods of repeating chord successions are used to refine the state space to enhance the detection performance. Furthermore, a post-processing technique is used to map detected segments into sections in a song. Experimental results from test musical audio data are given to demonstrate the performance of the proposed method.

Similarity matrix processing for music structure analysis

A new framework for multiscale video coding is proposed in this research with the objective to achieve efficient video coding with spatial scalability. In this framework, multiscale motion compensation is performed to remove temporal redundancy, and both the motion field and the displacement frame difference (DFD) are compressed via wavelet transform, successive embedded quantization and entropy coding for multiscale representation and decoding. The proposed new method does not only secure the prediction loop of lower resolution video but also provides an efficient prediction from coarser to finer resolutions.

Multiscale video compression using wavelet transform and motion compensation

Traditional image steganalysis techniques are conducted with respect to the entire image. In this work, we aim to differentiate a stego image from its cover image based on steganalysis results of decomposed image blocks. As a natural image often consists of heterogeneous regions, its decomposition will lead to smaller image blocks, each of which is more homogeneous. We classify these image blocks into multiple classes and find a classifier for each class to decide whether a block is from a cover or stego image. Consequently, the steganalysis of the whole image can be conducted by fusing steganalysis results of all image blocks through a voting process. Experimental results will be given to show the advantage of the proposed block-based image steganalysis approach.

/pdf/block-based-image-steganalysis-algorithm-and-performance-37sf2hecgz.pdf

C.-C. Jay Kuo

Papers

Inference of missing SNPs and information quantity measurements for haplotype blocks

Coexistence Wi-Fi MAC Design for Mitigating Interference Caused by Collocated Bluetooth

Similarity matrix processing for music structure analysis

Multiscale video compression using wavelet transform and motion compensation

Block-based image steganalysis: Algorithm and performance evaluation