Studying Aesthetics in Photographic Images Using a Computational Approach
Aesthetics, in the world of art and photography, refers to the principles of the nature and appreciation of beauty. Judging beauty and other aesthetic qualities of photographs is a highly subjective task. Hence, there is no unanimously agreed standard for measuring aesthetic value. In spite of the lack of firm rules, certain features in photographic images are believed, by many, to please humans more than certain others. In this paper, we treat the challenge of automatically inferring aesthetic quality of pictures using their visual content as a machine learning problem, with a peer-rated online photo sharing Website as data source. We extract certain visual features based on the intuition that they can discriminate between aesthetically pleasing and displeasing images. Automated classifiers are built using support vector machines and classification trees. Linear regression on polynomial terms of the features is also applied to infer numerical aesthetics ratings. The work attempts to explore the relationship between emotions which pictures arouse in people, and their low-level content. Potential applications include content-based image retrieval and digital photography.

title: On Zador's entropy-constrained quantization theorem
abstract: Zador's classic result for the asymptotic high-rate behavior of entropy-constrained vector quantization is recast in a Lagrangian form which better matches the Lloyd algorithm used to optimize such quantizers. A proof that the result holds for a general class of distributions is sketched.

title: Unsupervised Multiresolution Segmentation for Images with Low Depth of Field
abstract: Unsupervised segmentation of images with low depth of field (DOF) is highly useful in various applications. This paper describes a novel multiresolution image segmentation algorithm for low DOF images. The algorithm is designed to separate a sharply focused object-of-interest from other foreground or background objects. The algorithm is fully automatic in that all parameters are image independent. A multi-scale approach based on high frequency wavelet coefficients and their statistics is used to perform context-dependent classification of individual blocks of the image. Unlike other edge-based approaches, our algorithm does not rely on the process of connecting object boundaries. The algorithm has achieved high accuracy when tested on more than 100 low DOF images, many with inhomogeneous foreground or background distractions. Compared with he state of the art algorithms, this new algorithm provides better accuracy at higher speed.

title: Real-Time Computerized Annotation of Pictures
abstract: Developing effective methods for automated annotation of digital pictures continues to challenge computer scientists. The capability of annotating pictures by computers can lead to breakthroughs in a wide range of applications, including Web image search, online picture-sharing communities, and scientific experiments. In this work, the authors developed new optimization and estimation techniques to address two fundamental problems in machine learning. These new techniques serve as the basis for the automatic linguistic indexing of pictures - real time (ALIPR) system of fully automatic and high-speed annotation for online pictures. In particular, the D2-clustering method, in the same spirit as K-Means for vectors, is developed to group objects represented by bags of weighted vectors. Moreover, a generalized mixture modeling technique (kernel smoothing as a special case) for nonvector data is developed using the novel concept of hypothetical local mapping (HLM). ALIPR has been tested by thousands of pictures from an Internet photo-sharing site, unrelated to the source of those pictures used in the training process. Its performance has also been studied at an online demonstration site, where arbitrary users provide pictures of their choices and indicate the correctness of each annotation word. The experimental results show that a single computer processor can suggest annotation terms in real time and with good accuracy.

title: A source coding approach to classification by vector quantization and the principle of minimum description length
abstract: An algorithm for supervised classification using vector quantization and entropy coding is presented. The classification rule is formed from a set of training data {(X/sub i/, Y/sub i/)}/sub i=1//sup n/, which are independent samples from a joint distribution P/sub XY/. Based on the principle of minimum description length (MDL), a statistical model that approximates the distribution P/sub XY/ ought to enable efficient coding of X and Y. On the other hand, we expect a system that encodes (X, Y) efficiently to provide ample information on the distribution P/sub XY/. This information can then be used to classify X, i.e., to predict the corresponding Y based on X. To encode both X and Y, a two-stage vector quantizer is applied to X and a Huffman code is formed for Y conditioned on each quantized value of X. The optimization of the encoder is equivalent to the design of a vector quantizer with an objective function reflecting the joint penalty of quantization error and misclassification rate. This vector quantizer provides an estimation of the conditional distribution of Y given X, which in turn yields an approximation to the Bayes classification rule. This algorithm, namely discriminant vector quantization (DVQ), is compared with learning vector quantization (LVQ) and CART/sup R/ on a number of data sets. DVQ outperforms the other two on several data sets. The relation between DVQ, density estimation, and regression is also discussed.

title: Asymptotic Performance of Vector Quantizers with a Perceptual Distortion Measure
abstract: Gersho's (1979) bounds on the asymptotic performance of vector quantizers are valid for vector distortions which are powers of the Euclidean norm. Yamada, Tazaki, and Gray (1980) generalized the results to distortion measures that are increasing functions of the norm of their argument. In both cases, the distortion is uniquely determined by the vector quantization error, i.e., the Euclidean difference between the original vector and the codeword into which it is quantized. We generalize these asymptotic bounds to input-weighted quadratic distortion measures and measures that are approximately output-weighted-quadratic when the distortion is small, a class of distortion measures often claimed to be perceptually meaningful. An approximation of the asymptotic distortion based on Gersho's conjecture is derived as well. We also consider the problem of source mismatch, where the quantizer is designed using a probability density different from the true source density. The resulting asymptotic performance in terms of distortion increase in decibels is shown to be linear in the relative entropy between the true and estimated probability densities.

title: Rule Evaluations, Attributes, and Rough Sets: Extension and a Case Study
abstract: Manually evaluating important and interesting rules generated from data is generally infeasible due to the large number of rules extracted. Different approaches such as rule interestingness measures and rule quality measures have been proposed and explored previously to extract interesting and high quality association rules and classification rules. Rough sets theory was originally presented as an approach to approximate concepts under uncertainty. In this paper, we explore rough sets based rule evaluation approaches in knowledge discovery. We demonstrate rule evaluation approaches through a real-world geriatric care data set from Dalhousie Medical School. Rough set based rule evaluation approaches can be used in a straightforward way to rank the importance of the rules. One interesting system developed along these lies in HYRIS (HYbrid Rough sets Intelligent System). We introduce HYRIS through a case study on survival analysis using the geriatric care data set.

title: A method to take account of inhomogeneity in mechanical component reliability calculations
abstract: This paper proposes a method by which material inhomogeneity may be taken into account in a reliability calculation. The method employs Monte-Carlo simulation; and introduces a material strength index, and a standard deviation of material strength to model the variation in the strength of a component throughout its volume. The method is compared to conventional load-strength interference theory. The results are identical for the case of homogeneous material, but reliability is shown to reduce for the same load as the component volume increases. The case of a tensile bar is used to explore the variation of reliability with component volume.

title: Towards evolving Web sites into grid services environment
abstract: Grid services are emerged by integrating grid computing and Web services to perform a seamless information processing system across distributed, heterogeneous, dynamic virtual organisations that the user can access from any location. The Web Service Resource Framework (WSRF) was announced as a new way for manipulating "stateful resources" to perform grid services. It defines the concept of "stateful resources" and how they can be discovered, queried and manipulated via Web services. In this paper, we proposed a grid services-oriented reengineering approach to create stateful resources from conventional HTML Web sites, which applies hierarchical cluster and wrapper techniques to extract and translate Web sites resources. It supports services identification and packaging and archives Web site evolution into grid services environment by exploiting WRSF.

title: Evolutionary Optimisation of Distributed Energy Resources
abstract: Genetic optimisation is used to minimise operational costs across a system of electrical loads and generators controlled by local intelligent agents and connected to the electricity grid at market rates. Experimental results in a simulated environment show that coordinated market-sensitive behaviours are achieved. A large network of 500 loads and generators, each characterised by different randomly selected parameters, was optimised using a two-stage genetic algorithm to achieve scalability.

title: RACCOON: a peer-based system for data integration and sharing
abstract: Recent database applications see the emerging need to support data integration in distributed, peer-to-peer environments, in which autonomous peers (sources) connected by a network are willing to exchange data and services with each other. To address related research challenges, we are developing a system called "RACCOON", which allows different sources to integrate and share their data. We use an application to show several important features of the RACCOON system. The system also suggests semantic mappings for the user to choose. We show the two different querying modes, particularly how a query is expanded using the semantic mappings in the extended querying mode to compute as many answers to the query as possible.