Unsupervised Multiresolution Segmentation for Images with Low Depth of Field
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: 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: SIMPLIcity: Semantics-sensitive Integrated Matching for Picture Libraries
abstract: We present here SIMPLIcity (Semantics-sensitive Integrated Matching for Picture LIbraries), an image retrieval system using semantics classification and integrated region matching (IRM) based upon image segmentation. The SIMPLIcity system represents an image by a set of regions, roughly corresponding to objects, which are characterized by color, texture, shape, and location. The system classifies images into categories which are intended to distinguish semantically meaningful differences, such as textured versus nontextured, indoor versus outdoor, and graph versus photograph. Retrieval is enhanced by narrowing down the searching range in a database to a particular category and exploiting semantically-adaptive searching methods. A measure for the overall similarity between images, the IRM distance, is defined by a region-matching scheme that integrates properties of all the regions in the images. This overall similarity approach reduces the adverse effect of inaccurate segmentation, helps to clarify the semantics of a particular region, and enables a simple querying interface for region-based image retrieval systems. The application of SIMPLIcity to a database of about 200,000 general-purpose images demonstrates accurate retrieval at high speed. The system is also robust to image alterations.

title: Two-scale image retrieval with significant meta-information feedback
abstract: A two-scale image retrieval system is developed to provide efficient search in large-scale databases as well as flexibility for users to incorporate ubjective preferences during retrieval. A new clustering method is developed for images each characterized by a varying number of weighted feature vectors. Furthermore, significant meta-information is mined within every cluster. A scanning mode of retrieval is created using cluster centers, which serve as a low scale version of a database in contrast to original images. In particular, users are presented with representative images of highly ranked clusters along with prominent meta-information. This retrieval approach enables users to quickly examine a large and diverse portion of a database surrounding a query and to learn about hidden connections between visual patterns and non-imagery types of data. The clusters formed also facilitate fast search in the case of individual image-based retrieval by filtering out images whose cluster centers are far from the query. The two-scale retrieval system has been implemented on a fine art painting database. Advantages of the system have been demonstrated by quantitative evaluation of the retrieval performance.

title: Text and picture segmentation by the distribution analysis of wavelet coefficients
abstract: This paper presents an algorithm to segment text and picture in an image using two features based on the statistical distribution of the wavelet coefficients in high frequency bands. The algorithm breaks the image into blocks and classifies every block as background, text or picture according to the two features. The block size is variable so that the segmentation can be accurate at the boundary of two types and avoids misclassifying due to over-localized region analysis.

title: The Story Picturing Engine---a system for automatic text illustration
abstract: We present an unsupervised approach to automated story picturing. Semantic keywords are extracted from the story, an annotated image database is searched. Thereafter, a novel image ranking scheme automatically determines the importance of each image. Both lexical annotations and visual content play a role in determining the ranks. Annotations are processed using the Wordnet. A mutual reinforcement-based rank is calculated for each image. We have implemented the methods in our Story Picturing Engine (SPE) system. Experiments on large-scale image databases are reported. A user study has been performed and statistical analysis of the results has been presented.

title: A Self-Adjusting Algorithm for Driver Scheduling
abstract: Public transport driver scheduling is a world wide problem, which is NP-hard. Although some mathematically based methods are being used in the transport industry, there is still much scope for improvements. This paper presents a novel evolutionary approach that simulates the self-adjusting process on a single schedule. Five factors characterized by fuzzy membership functions are first aggregated to evaluate the shift structure. This evaluating function is incorporated into a constructing heuristic to make shift selection. A self-adjusting algorithm is then designed to guide the constructing heuristic to improve a given initial schedule iteratively. In each generation an unfit portion of the working schedule is removed. Broken schedules are repaired by the constructing heuristic until stopping condition is met. Experimental results on real-world driver scheduling problems has demonstrated the success of the proposed approach.

title: Influence and conditional influence-new interestingness measures in association rule mining
abstract: Discusses the issues of interestingness in association rule mining. First, a rule is possibly redundant or misleading even if it possesses high degrees of confidence and support. Second, association rules do not reflect the effect of negatively influential facts. Such problems are related to confidence deviation. In the paper, therefore, two new measures of interestingness, namely influence and conditional influence, are introduced to represent the effect of the antecedent on the consequent. Furthermore, the mining algorithms are extended accordingly such that certain redundant rules can be eliminated and negatively influential rules may be discovered.

title: Introducing a Rule Importance Measure
abstract: Association rule algorithms often generate an excessive number of rules, many of which are not significant. It is difficult to determine which rules are more useful, interesting and important. We introduce a rough set based Rule Importance Measure to select the most important rules. We use ROSETTA software to generate multiple reducts. Apriori association rule algorithm is then applied to generate rule sets for each data set based on each reduct. Some rules are generated more frequently than the others among the total rule sets. We consider such rules as more important. We define rule importance as the frequency of an association rule generated across all the rule sets. Rule importance is different from either rule interestingness measures or rule quality measures because of their application tasks, the processes where the measures are applied and the contents they measure. The experimental results from an artificial data set, UCI machine learning datasets and an actual geriatric care medical data set show that our method reduces the computational cost for rule generation and provides an effective measure of how important is a rule.

title: Compression of the layered depth image
abstract: A layered depth image (LDI) is a new popular representation and rendering method for objects with complex geometries. Similar to a 2D image, the LDI consists of an array of pixels. However, unlike the 2D image, an LDI pixel has depth information, and there are multiple layers at a pixel location. In this paper, we develop a novel LDI compression algorithm that handles the multiple layers and the depth coding. The algorithm records the number of LDI layers at each pixel location, and compresses LDI color and depth components separately. For LDI layer with sparse pixels, the data is aggregated and then encoded. An empirical rate-distortion model is used to optimally allocate bits among different components. Compared with benchmark compression tools such as JPEG-2000 and MPEG-4, our scheme improves the compression performance significantly.

title: Modeling the effects of elevation data resolution on the performance of topography-based watershed runoff simulation
abstract: The spatial uncertainty of a topography based rainfall runoff model (TOPMODEL) is addressed in this study to assess its variability in simulating watershed hydrologic response with regards to the change of digital elevation model (DEM) resolution. Twelve DEM realizations of different grid sizes ranging from 30m to 3000m for each of two case watersheds are used for comparative examinations. The study shows that DEM grid size has significant influence on the topographic index distribution which represents the effect of topography on watershed hydrology in TOPMODEL. The smoothing effect of grid size increase may result in deteriorated topographic index distributions at coarse resolutions as the ratio of grid cell area to watershed area gets larger. The simulated discharges and model efficiencies using a same set of TOPMODEL parameters are sensitive to DEM grid size especially at coarse resolutions. This sensitivity, however, can be moderated by parameter calibrations as the optimization runs show that fairly equal efficiencies can be preserved by the compensation effect of transmissivity parameter T"0 within a large extent of DEM resolution for each watershed. The interaction between T"0 and the topographic index distribution with respect to TOPMDOEL model performance is also examined. It is found that both study watersheds demonstrate a similar pattern of change in model performance along with the increase of the grid-to-watershed ratio. The analysis reveals that the ratio poses an important factor in controlling the effect of DEM grid size on TOPMODEL performance. A ratio of less than 5% is suggested in DEM resolution selection for TOPMODEL applications based on the results of this study.