Asymptotic Performance of Vector Quantizers with a Perceptual Distortion Measure
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: 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: 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: 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: 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: 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: 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: Genescene: biomedical text and data mining
abstract: To access the content of digital texts efficiently, it is necessary to provide more sophisticated access than keyword based searching. Genescene provides biomedical researchers with research findings and background relations automatically extracted from text and experimental data. These provide a more detailed overview of the information available. The extracted relations were evaluated by qualified researchers and are precise. A qualitative ongoing evaluation of the current online interface indicates that this method to search the literature is more useful and efficient than keyword based searching.

title: Product accumulate codes: a class of codes with near-capacity performance and low decoding complexity
abstract: We propose a novel class of provably good codes which are a serial concatenation of a single-parity-check (SPC)-based product code, an interleaver, and a rate-1 recursive convolutional code. The proposed codes, termed product accumulate (PA) codes, are linear time encodable and linear time decodable. We show that the product code by itself does not have a positive threshold, but a PA code can provide arbitrarily low bit-error rate (BER) under both maximum-likelihood (ML) decoding and iterative decoding. Two message-passing decoding algorithms are proposed and it is shown that a particular update schedule for these message-passing algorithms is equivalent to conventional turbo decoding of the serial concatenated code, but with significantly lower complexity. Tight upper bounds on the ML performance using Divsalar's (1999) simple bound and thresholds under density evolution (DE) show that these codes are capable of performance within a few tenths of a decibel away from the Shannon limit. Simulation results confirm these claims and show that these codes provide performance similar to turbo codes but with significantly less decoding complexity and with a lower error floor. Hence, we propose PA codes as a class of prospective codes with good performance, low decoding complexity, regular structure, and flexible rate adaptivity for all rates above 1/2.

title: AFBV: a scalable packet classification algorithm
abstract: Packet classification is a central function of a number of network activities, such as routing and firewall management. The packet classifier has a ordered rule database, one rule for each type of packets, and all arriving packets are classified into equivalent classes based on the first rule they match. With the development of the Internet, the database size can only grow with time. Consequently, scalability becomes an important issue for packet classification algorithm. This project is addressing the scalability problem in the selection of rules for packet classification. There is significant previous work in this area. Much previous work executed in linear time with the number of rules. Our work is an improvement on the ABV scheme of Baboescu and Varghese , which in turn is an improvement in the straightforward linear Lucent bit vector scheme (BV) . The BV scheme produces bit vectors of all rules for each field, and selects the rules applicable to the complete header by first finding the bit vectors corresponding to each field, then intersecting them, and the first set bit in the consequent bit vector implies the position of the rule in the database. ABV made two observations: first the set bits in the bit vectors are sparse and second a packet matches no more than a few rules. Hence, ABV improves BV to achieve logarithmic time in two steps. First, ABV rearranges the rules by sorting, so that those rules that match specific prefixes are near each other in the list. Second, it aggregates bits in each field to reduce memory accesses, but increasing the possibility of false matches. The rule rearrangement mitigates the false matches to some extent, but also incurs additional cost. First, rule rearrangement makes it necessary to find all matches, instead of first match in BV. Second, it is not easy to find an effective rearrangement method. Third, rules need to be mapped back to the original order so as to find the rule with the lowest order. Our scheme, the Aggregated and Folded Bit Vector algorithm (AFBV) inherits the idea of bit vector and aggregation from BV and ABV, but discards rule rearrangement and introduce a new concept: folding. All bits in a predefined range r (r is called folding range, usually equal to multiple aggregate sizes), whose positions have the same mod-f value are OR-ed together, the result occupying that position mod f, thus folding the bit vector (f is called folding size). Only when there are at least one bit set at the same mod-f position can one get false matches, while in ABV, a match among any bits within an aggregation group can lead to a false match. Once there is a match in the folded bit vector at position k (k is inclusively between 0 and f-1), the possible real match position must take the form (I · f + k). Therefore, folding helps not only filter out false matches but also locate the real match positions. This scheme will also not produce any false negatives (missing a match). Thus, correctness is guaranteed. Besides the folding range and the folding size, the number of folding is another important parameter in AFBV. By using multiple folding with different sizes, we can enhance the detecting and locating ability of the folded vectors. First, only if there are matches in all the intersections of the folded vectors may there be real matches in the original vectors. The gain here is that mostly we do not need to fetch the whole original bit vector to conclude a false match as required in BV and ABV. Second, with proper selection on the folding sizes, multiple folding greatly reduces the number of the possible match positions than single folding. The gain here is that in case of real matches, we only need to fetch parts instead of the whole original bit vectors. In addition to the aggregate bit vectors, AFBV needs to store the folded bit vectors, which incurs about 10% additional storage requirements, and could be even smaller. AFBV offers the following benefits: Only the first matched rule need to be found; no mapping back cost is needed; without rule rearrangement, pre-processing is much simple; a large aggregate size can be applied because the folded vectors reduce false matches. In our simulations of relative performance we compare the Lucent BV algorithm, the ABV algorithm and our AFBV algorithm, and evaluate both performance and space utilization. Preliminary results on different-sized databases show that AFBV outperforms ABF and BV on average, and can achieve the similar level of performance in worst cases, with about a 10% increase in memory requirements, for the additional, folded vectors.

title: On (g, f, n)-Critical Graphs