An Enhanced Method For Evaluating Automatic Video Summaries

Evaluation of automatic video summaries is a challenging problem. In the past years, some evaluation methods are presented that utilize only a single feature like color feature to detect similarity between automatic video summaries and ground-truth user summaries. One of the drawbacks of using a single feature is that sometimes it gives a false similarity detection which makes the assessment of the quality of the generated video summary less perceptual and not accurate. In this paper, a novel method for evaluating automatic video summaries is presented. This method is based on comparing automatic video summaries generated by video summarization techniques with ground-truth user summaries. The objective of this evaluation method is to quantify the quality of video summaries, and allow comparing different video summarization techniques utilizing both color and texture features of the video frames and using the Bhattacharya distance as a dissimilarity measure due to its advantages. Our Experiments show that the proposed evaluation method overcomes the drawbacks of other methods and gives a more perceptual evaluation of the quality of the automatic video summaries.Comment: This paper has been withdrawn by the author due to some errors and incomplete stud

A framework for quantitative analysis of user-generated spatial data

This paper proposes a new framework for automated analysis of game-play metrics for aiding game designers in finding out the critical aspects of the game caused by factors like design modications, change in playing style, etc. The core of the algorithm measures similarity between spatial distribution of user generated in-game events and automatically ranks them in order of importance. The feasibility of the method is demonstrated on a data set collected from a modern, multiplayer First Person Shooter, together with application examples of its use. The proposed framework can be used to accompany traditional testing tools and make the game design process more efficient

Using basic image features for texture classification

Representing texture images statistically as histograms over a discrete vocabulary of local features has proven widely effective for texture classification tasks. Images are described locally by vectors of, for example, responses to some filter bank; and a visual vocabulary is defined as a partition of this descriptor-response space, typically based on clustering. In this paper, we investigate the performance of an approach which represents textures as histograms over a visual vocabulary which is defined geometrically, based on the Basic Image Features of Griffin and Lillholm (Proc. SPIE 6492(09):1-11, 2007), rather than by clustering. BIFs provide a natural mathematical quantisation of a filter-response space into qualitatively distinct types of local image structure. We also extend our approach to deal with intra-class variations in scale. Our algorithm is simple: there is no need for a pre-training step to learn a visual dictionary, as in methods based on clustering, and no tuning of parameters is required to deal with different datasets. We have tested our implementation on three popular and challenging texture datasets and find that it produces consistently good classification results on each, including what we believe to be the best reported for the KTH-TIPS and equal best reported for the UIUCTex databases

On Measures of Behavioral Distance between Business Processes

The desire to compute similarities or distances between business processes arises in numerous situations such as when comparing business processes with reference models or when integrating business processes. The objective of this paper is to develop an approach for measuring the distance between Business Processes Models (BPM) based on the behavior of the business process only while abstracting from any structural aspects of the actual model. Furthermore, the measure allows for assigning more weight to parts of a process which are executed more frequently and can thus be considered as more important. This is achieved by defining a probability distribution on the behavior allowing the computation of distance metrics from the field of statistics

On mass-constraints implied by the relativistic precession model of twin-peak quasi-periodic oscillations in Circinus X-1

Boutloukos et al. (2006) discovered twin-peak quasi-periodic oscillations (QPOs) in 11 observations of the peculiar Z-source Circinus X-1. Among several other conjunctions the authors briefly discussed the related estimate of the compact object mass following from the geodesic relativistic precession model for kHz QPOs. Neglecting the neutron star rotation they reported the inferred mass M_0 = 2.2 +/- 0.3 M_\sun. We present a more detailed analysis of the estimate which involves the frame-dragging effects associated with rotating spacetimes. For a free mass we find acceptable fits of the model to data for (any) small dimensionless compact object angular momentum j=cJ/GM^2. Moreover, quality of the fit tends to increase very gently with rising j. Good fits are reached when M ~ M_0[1+0.55(j+j^2)]. It is therefore impossible to estimate the mass without the independent knowledge of the angular momentum and vice versa. Considering j up to 0.3 the range of the feasible values of mass extends up to 3M_\sun. We suggest that similar increase of estimated mass due to rotational effects can be relevant for several other sources.Comment: 10 pages, 9 figures (in colour

Minimum Distance Estimation of Milky Way Model Parameters and Related Inference

We propose a method to estimate the location of the Sun in the disk of the Milky Way using a method based on the Hellinger distance and construct confidence sets on our estimate of the unknown location using a bootstrap based method. Assuming the Galactic disk to be two-dimensional, the sought solar location then reduces to the radial distance separating the Sun from the Galactic center and the angular separation of the Galactic center to Sun line, from a pre-fixed line on the disk. On astronomical scales, the unknown solar location is equivalent to the location of us earthlings who observe the velocities of a sample of stars in the neighborhood of the Sun. This unknown location is estimated by undertaking pairwise comparisons of the estimated density of the observed set of velocities of the sampled stars, with densities estimated using synthetic stellar velocity data sets generated at chosen locations in the Milky Way disk according to four base astrophysical models. The "match" between the pair of estimated densities is parameterized by the affinity measure based on the familiar Hellinger distance. We perform a novel cross-validation procedure to establish a desirable "consistency" property of the proposed method.Comment: 25 pages, 10 Figures. This version incorporates the suggestions made by the referees. To appear in SIAM/ASA Journal on Uncertainty Quantificatio

An Automatic Similarity Detection Engine Between Sacred Texts Using Text Mining and Similarity Measures

Is there any similarity between the contexts of the Holy Bible and the Holy Quran, and can this be proven mathematically? The purpose of this research is using the Bible and the Quran as our corpus, we explore the performance of various feature extraction and machine learning techniques. The unstructured nature of text data adds an extra layer of complexity in the feature extraction task, and the inherently sparse nature of the corresponding data matrices makes text mining a distinctly difficult task. Among other things, We assess the difference between domain-based syntactic feature extraction and domain-free feature extraction, and then use a variety of similarity measures like Euclidean, Hillinger, Manhattan, cosine, Bhattacharyya, symmetries kullback-leibler, Jensen Shannon, probabilistic chi-square and clark. For a similarity to identify similarities and differences between sacred texts. Initially I started by comparing chapters of two raw text using the proximity measures to visualize their behaviors on high dimensional and spars space. It was apparent there was similarity between some of the chapters, but it was not conclusive. Therefore, there was a need to clean the noise using the so called Natural Language processing (NLP). For example, to minimize the size of two vectors, We initiated lists of similar vocabulary that worded differently in both texts but indicates the same exact meaning. Therefore, the program would recognize Lord as God in the Holy Bible and Allah as God in the Quran and Jacob as prophet in bible and Yaqub as a prophet in Quran. This process was completed many times to give relative comparisons on a variety of different words. After completion of the comparison of the raw texts, the comparison was completed for the processed text. The next comparison was completed using probabilistic topic modeling on feature extracted matrix to project the topical matrix into low dimensional space for more dense comparison. Among the distance measures intrdued to the sacred corpora, the analysis of similarities based on the probability based measures like Kullback leibler and Jenson shown the best result. Another similarity result based on Hellinger distance on the CTM also shows good discrimination result between documents. This work started with a believe that if there is intersection between Bible and Quran, it will be shown clearly between the book of Deuteronomy and some Quranic chapters. It is now not only historically, but also mathematically is correct to say that there is much similarity between the Biblical and Quranic contexts more than the similarity within the holy books themselves. Furthermore, it is the conclusion that distances based on probabilistic measures such as Jeffersyn divergence and Hellinger distance are the recommended methods for the unstructured sacred texts

The Burbea-Rao and Bhattacharyya centroids

We study the centroid with respect to the class of information-theoretic Burbea-Rao divergences that generalize the celebrated Jensen-Shannon divergence by measuring the non-negative Jensen difference induced by a strictly convex and differentiable function. Although those Burbea-Rao divergences are symmetric by construction, they are not metric since they fail to satisfy the triangle inequality. We first explain how a particular symmetrization of Bregman divergences called Jensen-Bregman distances yields exactly those Burbea-Rao divergences. We then proceed by defining skew Burbea-Rao divergences, and show that skew Burbea-Rao divergences amount in limit cases to compute Bregman divergences. We then prove that Burbea-Rao centroids are unique, and can be arbitrarily finely approximated by a generic iterative concave-convex optimization algorithm with guaranteed convergence property. In the second part of the paper, we consider the Bhattacharyya distance that is commonly used to measure overlapping degree of probability distributions. We show that Bhattacharyya distances on members of the same statistical exponential family amount to calculate a Burbea-Rao divergence in disguise. Thus we get an efficient algorithm for computing the Bhattacharyya centroid of a set of parametric distributions belonging to the same exponential families, improving over former specialized methods found in the literature that were limited to univariate or "diagonal" multivariate Gaussians. To illustrate the performance of our Bhattacharyya/Burbea-Rao centroid algorithm, we present experimental performance results for $k$-means and hierarchical clustering methods of Gaussian mixture models.Comment: 13 page

Definition of an automated Content-Based Image Retrieval (CBIR) system for the comparison of dermoscopic images of pigmented skin lesions

<p>Abstract</p> <p>Background</p> <p>New generations of image-based diagnostic machines are based on digital technologies for data acquisition; consequently, the diffusion of digital archiving systems for diagnostic exams preservation and cataloguing is rapidly increasing. To overcome the limits of current state of art text-based access methods, we have developed a novel content-based search engine for dermoscopic images to support clinical decision making.</p> <p>Methods</p> <p>To this end, we have enrolled, from 2004 to 2008, 3415 caucasian patients and collected 24804 dermoscopic images corresponding to 20491 pigmented lesions with known pathology. The images were acquired with a well defined dermoscopy system and stored to disk in 24-bit per pixel TIFF format using interactive software developed in C++, in order to create a digital archive.</p> <p>Results</p> <p>The analysis system of the images consists in the extraction of the low-level representative features which permits the retrieval of similar images in terms of colour and texture from the archive, by using a hierarchical multi-scale computation of the Bhattacharyya distance of all the database images representation with respect to the representation of user submitted (query).</p> <p>Conclusion</p> <p>The system is able to locate, retrieve and display dermoscopic images similar in appearance to one that is given as a query, using a set of primitive features not related to any specific diagnostic method able to visually characterize the image. Similar search engine could find possible usage in all sectors of diagnostic imaging, or digital signals, which could be supported by the information available in medical archives.</p