Manuela Montangero

STIMO: STIll and MOving video storyboard for the web scenario

In the current Web scenario a video browsing tool that produces on-the-fly storyboards is more and more a need. Video summary techniques can be helpful but, due to their long processing time, they are usually unsuitable for on-the-fly usage. Therefore, it is common to produce storyboards in advance, penalizing users customization. The lack of customization is more and more critical, as users have different demands and might access the Web with several different networking and device technologies. In this paper we propose STIMO, a summarization technique designed to produce on-the-fly video storyboards. STIMO produces still and moving storyboards and allows advanced users customization (e.g., users can select the storyboard length and the maximum time they are willing to wait to get the storyboard). STIMO is based on a fast clustering algorithm that selects the most representative video contents using HSV frame color distribution. Experimental results show that STIMO produces storyboards with good quality and in a time that makes on-the-fly usage possible.

VISTO: visual storyboard for web video browsing

Web video browsing is rapidly becoming a very popular activity in the Web scenario, causing the production of a concise video content representation a real need. Currently, static video summary techniques can be used to this aim. Unfortunately, they require long processing time and hence all the summaries are produced in advance without any users customization. With an increasing number of videos and with the large users heterogeneousness, this is a burden. In this paper we propose VISTO, a summarization technique that produces customized on-the-fly video storyboards. The mechanism uses a fast clustering algorithm that selects the most representative frames using their HSV color distribution and allows users to select the storyboard length and the processing time. An objective and subjective evaluation shows that the storyboards are produced with good quality and in a time that allows on-the-fly usage.

The plurality problem with three colors and more

The plurality problem is a game between two participants: Paul and Carole. We are given n balls, each of them is colored with one out of c colors. At any step of the game, Paul chooses two balls and asks whether they are of the same color, whereupon Carole answers yes or no. The game ends when Paul either produces a ball a of the plurality color (meaning that the number of balls colored like a exceeds those of the other colors), or when Paul states that there is no plurality. How many questions Lc(n) does Paul have to ask in the worst case?For c = 2, the problem is equivalent to the well-known majority problem which has already been solved (Combinatorica 11 (1991) 383-387). In this paper we show that 3 ⌊n/2⌋-2 ≤ L3(n) ≤ ⌊5n/3⌋ - 2. Moreover, for any c ≤ n, we show that surprisingly the naive algorithm for the plurality problem is asymptotically optimal.

TRank: Ranking Twitter users according to specific topics

Twitter is the most popular real-time micro-blogging service and it is a platform where users provide and obtain information at rapid pace. In this scenario, one of the biggest challenge is to find a way to automatically identify the most influential users of a given topic. Currently, there are several approaches that try to address this challenge using different Twitter signals (e.g., number of followers, lists, metadata), but results are not clear and sometimes conflicting. In this paper, we propose TRank, a novel method designed to address the problem of identifying the most influential Twitter users on specific topics identified with hashtags. The novelty of our approach is that it combines different Twitter signals (that represent both the user and the users tweets) to provide three different indicators that are intended to capture different aspects of being influent. The computation of these indicators is not based on the magnitude of the Twitter signals alone, but they are computed taking into consideration also human factors, as for example the fact that a user with many active followings might have a very noisy time lime and, thus, miss to read many tweets. The experimental assessment confirms that our approach provides results that are more reasonable than the one obtained by mechanisms based on the sole magnitude of data.

Optimal decision trees for local image processing algorithms

In this paper we present a novel algorithm to synthesize an optimal decision tree from OR-decision tables, an extension of standard decision tables, complete with the formal proof of optimality and computational cost analysis. As many problems which require to recognize particular patterns can be modeled with this formalism, we select two common binary image processing algorithms, namely connected components labeling and thinning, to show how these can be represented with decision tables, and the benefits of their implementation as optimal decision trees in terms of reduced memory accesses. Experiments are reported, to show the computational time improvements over state of the art implementations.

K-Boost: a scalable algorithm for high-quality clustering of microarray gene expression data.

Microarray technology for profiling gene expression levels is a popular tool in modern biological research. Applications range from tissue classification to the detection of metabolic networks, from drug discovery to time-critical personalized medicine. Given the increase in size and complexity of the data sets produced, their analysis is becoming problematic in terms of time/quality trade-offs. Clustering genes with similar expression profiles is a key initial step for subsequent manipulations and the increasing volumes of data to be analyzed requires methods that are at the same time efficient (completing an analysis in minutes rather than hours) and effective (identifying significant clusters with high biological correlations). In this paper, we propose K-Boost, a clustering algorithm based on a combination of the furthest-point-first (FPF) heuristic for solving the metric k-center problem, a stability-based method for determining the number of clusters, and a k-means-like cluster refinement. K-Boost runs in O (|N| x k) time, where N is the input matrix and k is the number of proposed clusters. Experiments show that this low complexity is usually coupled with a very good quality of the computed clusterings, which we measure using both internal and external criteria. Supporting data can be found as online Supplementary Material at www.liebertonline.com.

Approximation algorithms for a hierarchically structured bin packing problem

In this paper we study a variant of the bin packing problem in which the items to be packed are structured as the leaves of a tree. The problem is motivated by document organization and retrieval. We show that the problem is NP-hard and we give approximation algorithms for the general case and for the particular case in which all the items have the same size.

The Plurality Problem with Three Colors

The plurality problem with three colors is a game between two participants: Paul and Carol. Suppose we are given n balls colored with three colors. At any step of the game, Paul chooses two balls and asks whether they are of the same color, whereupon Carol answers yes or no. The game ends when Paul either produces a ball a of the plurality color (meaning that the number of balls colored like a exceeds those of the other colors), or when Paul states that there is no plurality. How many questions L(n) does Paul have to ask in the worst case? We show that \(3\lfloor n/2 \rfloor - 2 \leq L(n) \leq \lfloor 5n/3 \rfloor - 2\)

FPF-SB: a scalable algorithm for microarray gene expression data clustering

Efficient and effective analysis of large datasets from microarray gene expression data is one of the keys to time-critical personalized medicine. The issue we address here is the scalability of the data processing software for clustering gene expression data into groups with homogeneous expression profile. In this paper we propose FPF-SB, a novel clustering algorithm based on a combination of the Furthest-Point-First (FPF) heuristic for solving the k- center problem and a stability-based method for determining the number of clusters k. Our algorithm improves the state of the art: it is scalable to large datasets without sacrificing output quality.

The Use of Incentive Mechanisms in Multi-Channel Mobile Music Distribution

The music industry is planning to perform a significant shift toward the digital world by partnering cellphone network operators to build a mobile music market. In this paper we analyze the mobile music distribution strategy focusing on: the communication infrastructure, the pricing strategy and the copyright protection. Results of our analysis show that the strategy may be questioned and hence we propose a multi-channel distribution approach that makes use of both the cellphone network and the free-of-charge communication technologies provided in cellphones. Our multi-channel distribution strategy is coupled with an incentive mechanism that stimulates customers cooperation in the mobile scenario. An evaluation of our approach shows that all the entities involved in the mobile music distribution (customers, cellphone network providers and music stores) can benefit from using a multi-channel distribution approach