Automatic Website Summarization by Image Content: A Case Study with Logo and Trademark Images

We present a new algorithm to analyse information content in images acquired using automated fluorescence microscopy. The algorithm belongs to the group of autofocusing methods, but differs from its predecessors in that it can handle thick specimens and operate also in confocal mode. It measures the information content in images using a 'content function', which is essentially the same concept as a focus function. Unlike previously presented algorithms, this algorithm tries to find all significant axial positions in cases where the content function applied to real data is not unimodal, which is often the case. This requirement precludes using algorithms that rely on unimodality. Moreover, choosing a content function requires careful consideration, because some functions suppress local maxima. First, we test 19 content functions and evaluate their ability to show local maxima clearly. The results show that only six content functions succeed. To save time, the acquisition procedure needs to vary the step size adaptively, because a wide range of possible axial positions has to be passed so as not to miss a local maximum. The algorithm therefore has to assess the steepness of the content function online so that it can decide to use a bigger or smaller step size to acquire the next image. Therefore, the algorithm needs to know about typical behaviour of content functions. We show that for normalized variance, one of the most promising content functions, this knowledge can be obtained after normalizing with respect to the theoretical maximum of this function, and using hierarchical clustering. The resulting algorithm is more reliable and efficient than a simple procedure with constant steps.

Despite emerging of Web 2.0 applications and increasing requirements to well-behaved Web robots, malicious ones can reveal irreparable risks for Web sites. Regardless of behavior of Web robots, they may occupy bandwidth and reduce performance of Web servers. In spite of many prestigious researches trying to characterize Web visitors and classify them, there is a lack of concentration on feature selection to dynamically choose attributes used to describe Web sessions. On the other hand, depending on an accurate clustering technique, which can deal with huge number of samples in a reasonable amount of time, is practically important. Therefore, in this paper, a new algorithm, fuzzy rough set–Web robot detection (FRS-WRD), is proposed based on fuzzy rough set theory to better characterize and cluster Web visitors of three real Web sites. External evaluations show that in contrast to state-of-the-art algorithms, FRS-WRD achieves better results in terms of G-mean 95%, Jaccard 88%, entropy 0.36, and finally, purity 96%. Moreover, according to confusion matrixes, it can better detect malicious Web visitors.

In MRI, a trade-off exists between resolution and signal-to-noise ratio, since different fractions of the available scan time can be used to acquire data at higher spatial frequencies and to perform signal averaging. By comparing a wide variety of 3D isotropic MR scans with different combinations of SNR, resolution, and scan duration, the impact of this trade-off on the image information content was assessed. The information content of mouse brain, mouse whole-body, and human brain images was evaluated using a simple numerical approach, which sums the information contribution of each individual k-space data point. Results show that, with a fixed receiver bandwidth and field of view, the information content of trade-off images is always maximized when the SNR is equal to about 16. The optimal imaging resolution is dependent on the scan duration, as well as certain MR system properties, such as field strength and coil sensitivity. These properties are, however, easily accounted for with the acquisition of a single scout MR image, and the optimal imaging resolution can then be calculated using a simple mathematical relationship. If the imaging task is approached with a predetermined resolution requirement, the same scout scan can be used to calculate the scan duration that will provide the maximum possible information. Using these relationships to maximize the image information content is an excellent technique for guiding the initial selection of imaging parameters.

As the usage of Web Services proliferates dramatically, new tools to help quickly generate web services are needed. In this paper, we propose a methodology that helps to automatically generate Web Services from the FORM-based query interfaces of a web site. Since the majority of web data are rather hidden behind such a FORM interface, we believe turning such a human-oriented query interface into machine-oriented web services is an important problem. Toward this goal, we adopt the Wrapper technology successfully developed and deployed in Database community, and demonstrate how to generate Web Services components (e.g., WSDL, UDDI, SOAP) automatically. We present the overall architecture of our developed prototype and a few showcases based on real web sites.

Museums and the Web is an annual conference sponsored by Archives and Museum Informatics.The 99 conference provided us with an opportunity to reflect on how museums are being transformed by the Web as well as to understand the challenges that they are being presented with by the demand for parallel virtual programming

In this paper, we propose a retrieval system for logo and trademark images. One technique that has been proposed in the past employs the histogram of edge direction angles to index an image in a database of images. The histogram is called the Edge Directional Histogram (EDH). Our proposed technique is based on using the wavelet decomposition coefficients of an image to build a co-occurrence histogram. We call this histogram the wavelet co-occurrence histogram (WCH). By capturing the edge information and intensity variations in an image, as well as the spatial separation of these variations, the WCH presents a more accurate representation of the image features than the EDH. Results demonstrate that our retrieval system performs better than the EDH.

EBCOT, as the baseline algorithm of JPEG-2000 final draft, is an efficient image coding technique. It is inherently more error resilient than many other wavelet-based schemes due to its independent coding of blocks in each subband. However, the loss of data of a block, in any lower frequency subband, in EBCOT can still degrade the perceptual image quality considerably. As robust entropy codes are used, the information of image content can help recover damaged regions of blocks. This paper discusses the use of reversible variable length codes (RVLC) and data partitioning for coding lower frequency subbands in EBCOT, instead of arithmetic codes. The selection of RVLC is based on content of images, which is classified as active and shape. We have observed that the proposed approach has very little additional bit-rate overhead and improved performance in the presence of errors from the help of content.

Intermittent Unexplained Abdominal Pain: Is It Porphyria?

This study investigates the relation between objective and subjective parameters which relate to the quality of magnetic-resonance images, with a view to developing procedures for minimizing imaging time. The investigation uses high-resolution images of the thumb as example. Detection thresholds for an artificial lesion and ratings of image quality were obtained for a variety of images, with several experienced observers. In addition, the information content was calculated for each image, using the method developed by Fuderer (1988 IEEE Trans. Med. Imaging 7 368–80). Results suggest that information content can be used as a predictor of either detection threshold or quality rating, with a critical information content beyond which there is no significant improvement in either of these aspects of image quality. Since it is possible to estimate the effect of imaging variables on information content, it is possible to predict the effect of time-saving imaging strategies on image quality. A procedure is suggested for determining the combination of imaging variables which gives the shortest possible imaging time whilst retaining image quality.

This research entitled “The Usage of Affixes on Lyrics Songs Tulus in The Album Monokrom: a Morphologycal Study.” The method used in this study is a qualitative method. The data are taken from the lyrics songs Tulus in the Album Monokrom that exist in the internet site and artificial data (made by the author).The theory are used Affixes, prefiks, sufiks, and Confixs. Based on the research there is a lot of use of affixation in lyrics songs Tulus on the Monokrom album. The data are grouped into 3, ie prefixed word, suffixes and confix.the prefix found in 58 data consists of prefixes ber-, me-, di-, and ter-. The most common prefix is ber- , whereas the prefix whose frequency of occurrence is the smallest is prefix di-. The suffix found in 44 data, consisting of suffix -kan, -an, -nya and -i. The most commonly found suffix is the suffix -kan, whereas the suffix with the least frequency of occurrences is the suffix -nya. The confix found in 8 data, consists of words that have confix ke-an and per-an.

Enhancing the content and structure of a web site is a very important task which can help to maintain people visiting a web site and gain new visits (or customers). Web mining area helps to enhance a web site organization and contents using data mining algorithms. In particular we may perform Web Mining using a Self Organizing Feature Map (SOFM or SOM) it is always needed an analysis phase by experts. To help analysts to perform this phase after SOFMs' training, many post-processing techniques have been developed (component planes, labels, etc.); however, none of these techniques are useful when working in web mining for off-line enhancements of a web site. In this paper an algorithm called Reverse Cluster Analysis (RCA) will be provided. It aims to identify important web pages based on a self organizing feature map (SOFM) when performing web text mining (WTM) and web usage mining (WUM). We successfully applied this technique in a real web site to show its effectiveness. We have extended previous work performing a comparison with another unsupervised technique, administrators survey and an extended survey.

We present a new image search technique that, given a background image, returns compatible foreground objects for image compositing tasks. The compatibility of a foreground object and a background scene depends on various aspects such as semantics, surrounding context, geometry, style and color. However, existing image search techniques measure the similarities on only a few aspects, and may return many results that are not suitable for compositing. Moreover, the importance of each factor may vary for different object categories and image content, making it difficult to manually define the matching criteria. In this paper, we propose to learn feature representations for foreground objects and background scenes respectively, where image content and object category information are jointly encoded during training. As a result, the learned features can adaptively encode the most important compatibility factors. We project the features to a common embedding space, so that the compatibility scores can be easily measured using the cosine similarity, enabling very efficient search. We collect an evaluation set consisting of eight object categories commonly used in compositing tasks, on which we demonstrate that our approach significantly outperforms other search techniques.

Despite the business importance attached to the choice of features in organizational Web sites, most research on the subject is descriptive in nature. To our knowledge, no study has attempted to understand the underlying theoretical rationale explaining firms' choice of Web site features. Adoption of innovative systems by organizations is not always a “rational decision” based on the market innovation perspective; it may be based on the organizations' decision to conform to the “institutionalized norms” within or across the industries. In a similar vein, the underlying rationale for the firms' choice of Web site features may be either predominantly “rational” or “institutional.” We use Web content analysis to examine the dominant theoretical perspective guiding the organizational Web sites: rational (differentiation) or institutional (alikeness). For this, we analyze the data recorded from 243 Web sites: 91 information technology firms (IT industry), 67 business schools (education industry) and 85 banks (banking industry). Data pertaining to 20 features of Web sites are classified into information and interactive contents within and across three industries. Results suggest that Web site features are primarily explained by intra-industry norms of alikeness rather than inter-industry similarities, thereby supporting the preponderance of “institutional perspective” for Web site features within each of the three industries examined. In contrast, differences in Web site features across the three industries can be explained using the “rational perspective.” Thus, both rational and institutional perspectives serve as useful theoretical lenses for understanding choice of organizational Web site features. The study also delineates a set of implications for research and practice.

A fast simulated annealing algorithm is developed for automatic object recognition. The object recognition problem is addressed as the problem of best describing a match between a hypothesized object and an image. The normalized correlation coefficient is used as a measure of the match. Templates are generated on-line during the search by transforming model images. Simulated annealing reduces the search time by orders of magnitude with respect to an exhaustive search. The algorithm is applied to the problem of how landmarks, e.g., traffic signs, can be recognized by a navigating robot. We illustrate the performance of our algorithm with real-world images of complicated scenes with traffic signs. False positive matches occur only for templates with very small information content. To avoid false positive matches, we propose a method to select model images for robust object recognition by measuring the information content of the model images. The algorithm works well in noisy images for model images with high information content.< <ETX xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">&gt;</ETX>

A method for processing microfocus X-ray images is described. It is based on high-frequency filtration and morphological image processing, which increases the contrast of the X-ray details. One of the most informative X-ray techniques is microfocus X-ray. In some cases, microfocus X-ray images cannot be reliably analyzed due to the peculiarities of the shooting method. So, the main disadvantages of microfocus X-ray images are most often an uneven background, distorted brightness characteristics and the presence of noise. The proposed method for enhancing the contrast of fine image details is based on the idea of combining high-frequency filtering and morphological image processing. The method consists of the following steps: noise suppression in the image, high-frequency filtering, morphological image processing, obtaining the resulting image. As a result of applying the method, the brightness of the contours in the image is enhanced. In the resulting image, all objects will have double outlines. The method was tested in the processing of 50 chest radiographs of patients with various pathologies. Radiographs were performed at the Mariinsky Hospital of St. Petersburg using digital stationary and mobile X-ray machines. In most of the radiographs, it was possible to improve the images contrast, to highlight the objects boundaries. Besides, the method was applied in microfocus X-ray tomography to improve the information content of projection data and improve the reconstruction of the 3D image of the research object. In both the first and second cases, the method showed satisfactory results. The developed method makes it possible to significantly increase the information content of microfocus X-ray images. The obtained practical results make it possible to count on broad prospects for the method application, especially in microfocus X-ray.