In this article, we introduce a novel deterministic method based on Expectation Maximization (EM) to solve the rather complex problem of designing a tourist trip or Personalized Itinerary Recommendation (PIR). PIR objective is to recommend a personalized tour consisting of successive Points of Interest (POIs), which maximizes user satisfaction and respects user time-frame constraints. On top of that, the POIs are divided into categories, in order for travelers to be able to set limits on the maximum (and minimum) number of POIs that belong to one category and are included in the itinerary. In the proposed framework, emphasis is given on the POIs sequence selection, which exploits the customized POI recommendations offered by a recommender system. Additionally, the proposed methodology with POIs categories is able to solve the TourMustSee problem, so that the tour includes a set of POIs that must be visited. The proposed system has been successfully incorporated into a mobile app, offering a complete tourist trip design. The high performance, resilience, and computational efficiency of the proposed framework are demonstrated by experimental findings and comparisons to existing approaches on numerous synthetic and real datasets.

The authors define and approximately solve the problem of unsupervised image sorting that is considered as a kind of content‐based image clustering. The content‐based image sorting is the creation of a route that passes through all the images once, in such an order that the next one from the previous image has similar content. In the end, an image ordering (e.g. slideshow) is automatically produced, so that the images with similar content should be close to each other. This problem resembles the problem known in the literature as ‘travelling salesman problem’ (TSP). In this work, the authors have proposed two classes of methods (the nearest‐neighbour and genetic methods) that have also been applied on the TSP problem. Their benefits on computational efficiency and accuracy are discussed over six datasets that have been created from the GHIM‐10K dataset. The experimental results demonstrate that the proposed methods efficiently solve the image sorting problem, producing image sequences that almost agree with human intuition.

We present a region based method for segmenting and splitting images of cells in an automatic and unsupervised manner. The detection of cell nuclei is based on the Bradley's method. False positives are automatically identified and rejected based on shape and intensity features. Additionally, the proposed method is able to automatically detect and split touching cells. To do so, we employ a variant of a region based multi-ellipse fitting method that makes use of constraints on the area of the split cells. The quantitative assessment of the proposed method has been based on two challenging public datasets. This experimental study shows that the proposed method outperforms clearly existing methods for segmenting fluorescence microscopy images.

We present a solution to the problem of discovering all periodic segments of a video and of estimating their period in a completely unsupervised manner. These segments may be located anywhere in the video, may differ in duration, speed, period and may represent unseen motion patterns of any type of objects (e.g., humans, animals, machines, etc). The proposed method capitalizes on earlier research on the problem of detecting common actions in videos, also known as commonality detection or video co-segmentation. The proposed method has been evaluated quantitatively and in comparison to a baseline, power-spectrum-based approach, on two ground-truth-annotated datasets (MHAD202-v, PERTUBE). From those, PERTUBE has been compiled specifically for the purposes of this study and includes a collection of you tube videos that have been shot in the wild, with several periodic segments. The results of this evaluation demonstrate that the propose method outperforms the baseline considerably, especially in the more challenging PERTUBE dataset.

Given two action sequences, we are interested in spotting/co-segmenting all pairs of sub-sequences that represent the same action. We propose a totally unsupervised solution to this problem. No a-priori model of the actions is assumed to be available. The number of common sub-sequences may be unknown. The sub-sequences can be located anywhere in the original sequences, may differ in duration and the corresponding actions may be performed by a different person, in different style. We treat this type of temporal action co-segmentation as a stochastic optimization problem that is solved by employing Particle Swarm Optimization (PSO). The objective function that is minimized by PSO capitalizes on Dynamic Time Warping (DTW) to compare two action sub-sequences. Due to the generic problem formulation and solution, the proposed method can be applied to motion capture (i.e., 3D skeletal) data or to conventional RGB videos acquired in the wild. We present extensive quantitative experiments on standard data sets as well as on data sets we introduced in this paper. The obtained results demonstrate that the proposed method achieves a remarkable increase in co-segmentation quality compared to all tested state of the art methods.

In this paper, we propose an efficient in-DBMS solution for the problem of sub-trajectory clustering and outlier detection in large moving object datasets. The method relies on a two-phase process: a voting-and-segmentation phase that segments trajectories according to a local density criterion and trajectory similarity criteria, followed by a sampling-and-clustering phase that selects the most representative sub-trajectories to be used as seeds for the clustering process. Our proposal, called S 2 T-Clustering (for Sampling-based Sub-Trajectory Clustering) is novel since it is the first, to our knowledge, that addresses the pure spatiotemporal sub-trajectory clustering and outlier detection problem in a real-world setting (by ‘pure’ we mean that the entire spatiotemporal information of trajectories is taken into consideration). Moreover, our proposal can be efficiently registered as a database query operator in the context of extensible DBMS (namely, PostgreSQL in our current implementation). The effectiveness and the efficiency of the proposed algorithm are experimentally validated over synthetic and real-world trajectory datasets, demonstrating that S 2 T-Clustering outperforms an off-the-shelf in-DBMS solution using PostGIS by several orders of magnitude.

We propose a flow propagation algorithm (FlowPro) that finds the community surrounding a node in a complex network. In each iteration of the main process of FlowPro, the initial node propagates a flow that is shared among its neighbors. Each node is able to store, propagate to its neighbors, and return, part of the flow it receives to the initial node. When the algorithm converges, the flow stored in the nodes that belong to the community of the initial node, is generally higher than the flow stored in the rest of the graph nodes, thus the requested community emerges. The novelty of the proposed approach lies in the fact that FlowPro is local, allows to visualize the community and does not require the knowledge of the entire graph as most of the existing methods found in the literature. This makes possible the application of FlowPro in extremely large graphs or in cases where the entire graph is unknown like in most social networks.

In this paper, we present an efficient algorithm for synthesizing novel, arbitrarily long animations of periodic dances. The input to the proposed method is motion capture data acquired from markeless visual observations of a human performing a periodic dance. The provided human motion capture data are temporally segmented into the constituent periodic motion patterns. These are further organized in a motion graph that also represents possible transitions among them. Finally, an efficient algorithm exploits this representation to come up with a previously unseen sequence of motion patterns that are stitched seamlessly into a novel, realistic dance animation. Several experiments have been conducted with real recordings of Greek folk dances. The obtained results are very promising and indicate the efficacy of the proposed approach, as well as its tolerance to dynamic and noisy human motion capture input.

In this paper, we propose a framework for automatic enhancement and identification of the active sea faults, usually connected with seismic activity using bathymetric images. To our knowledge, the problem of automatic identification of sea faults based on an image analysis method is faced for the first time. First, we compute the Slope and Aspect images as well their derivatives and then we efficiently combine a rotation and scale invariant filter and a pixel labeling method, providing a robust detection of the sea faults. Experimental results on offshore real topographic images and their correlation with recent earthquake activity indicate the reliable performance of the proposed scheme.

Networks in various application domains present an internal structure, where nodes form groups of tightly connected components which are more loosely connected to the rest of the network. Several attempts have been made to provide a formal definition to the generally described "community finding" concept, providing different approaches. Some algorithms follow an iterative approach starting by characterizing either the entire network, or each individual node as community, and splitting [1] or merging communities respectively, producing a hierarchical tree of nested communities, called dendrogram. Several researchers aim to find the entire hierarchical community dendrogram [1] while others wish to identify only the optimal community partition. Some researchers aim at discovering distinct (non-overlapping) communities, while others allow for overlaps [2]. The Blondel algorithm described by Blondel et al. in [3], follows a bottom-up approach. Each node in the graph comprises a singleton community. Two communities are merged into one if the resulting community has larger modularity value that both the initial ones. This is a fast and accurate algorithm which, detects all communities in the graph. In suffers however in the sense that it constantly, during its execution, requires the knowledge of a global information of the graph, namely the number of its edges (which change as the algorithm modifies the graph), limiting its distributed nature. The Infomap algorithm [4] transforms the problem of community detection into efficiently compressing the structure of the graph, so that one can recovered almost the entire structure from the compressed form. This is achieved by minimizing a function that expresses the tradeoff between compression factor and loss of information (difference between the original graph and the reconstructed graph).

In this paper, we propose an efficient clustering algorithm that has been applied to the microaggregation problem. The goal is to partition N given records into clusters, each of them grouping at least K records, so that the sum of the within-partition squared error (SSE) is minimized. We propose a successive Group Selection algorithm that approximately solves the microaggregation problem in O(N2log N) time, based on sequential Minimization of SSE. Experimental results and comparisons to existing methods with similar computation cost on real and synthetic data sets demonstrate the high performance and robustness of the proposed scheme.

Moving Object Databases (MOD), although ubiquitous, still call for methods that will be able to understand, search, analyze, and browse their spatiotemporal content. In this paper, we propose a method for trajectory segmentation and sampling based on the representativeness of the (sub)trajectories in the MOD. In order to find the most representative subtrajectories, the following methodology is proposed. First, a novel global voting algorithm is performed, based on local density and trajectory similarity information. This method is applied for each segment of the trajectory, forming a local trajectory descriptor that represents line segment representativeness. The sequence of this descriptor over a trajectory gives the voting signal of the trajectory, where high values correspond to the most representative parts. Then, a novel segmentation algorithm is applied on this signal that automatically estimates the number of partitions and the partition borders, identifying homogenous partitions concerning their representativeness. Finally, a sampling method over the resulting segments yields the most representative subtrajectories in the MOD. Our experimental results in synthetic and real MOD verify the effectiveness of the proposed scheme, also in comparison with other sampling techniques.

In this paper, we propose an algorithm that finds the entire community structure of a network, based on local interactions between neighboring nodes and on an unsupervised centralized clustering algorithm. The novelty of the proposed approach is the fact that the algorithm is based on the use of network coordinates computed by a distributed algorithm. The current paper not only presents an efficient distributed community finding algorithm, but also demonstrates that network coordinates could be used to derive efficient solutions to a variety of problems. Experimental results and comparisons with other methods from literature are presented for a variety of benchmark graphs with known community structure, derived by varying a number of graph parameters.

In this paper, we propose a method for automatic enhancement and identification of partial curvilinear structures. The accurate identification of line structures in geophysical images plays an important role in geophysical interpretation and the detection of subsurface structures. The method was applied on geophysical images in an effort to recognize the linear patterns of subsurface architectural structures that exist in archaeological sites. To our knowledge, the problem of identification of curvilinear structures in geophysical images for archaeological sites is faced for the first time. The method efficiently combines a rotation- and scale-invariant filter and a pixel-labeling method, providing a robust enhancement and detection of mostly line structures in 2-D grayscale images, respectively. Experimental results on real and synthetic images and comparison with existing methods in the literature indicated the reliable performance of the proposed scheme.

We propose a general purpose image segmentation framework, which involves feature extraction and classification in feature space, followed by flooding and merging in spatial domain. Region growing is based on the computed local measurements and distances from the distribution of features describing the different classes. Using the properties of the label dependent distances spatial coherence is ensured, since the image features are described globally. The distribution of the features for the different classes are obtained by block-wise unsupervised clustering based on the construction of the minimum spanning tree of the blocks' grid using the Mallows distance and the equipartition of the resulting tree. The final clustering is obtained by using the k-centroids algorithm. With high probability and under topological constraints, connected components of the maximum likelihood classification map are used to compute a map of initially labelled pixels. An efficient flooding algorithm is introduced, namely, Priority Multi-Class Flooding Algorithm (PMCFA), that assign pixels to labels using Bayesian dissimilarity criteria. A new region merging method, which incorporates boundary information, is introduced for obtaining the final segmentation map. Therefore, the merging stage is based on region features and edge localization. Segmentation results on the Berkeley benchmark data set demonstrate the effectiveness of the proposed methods.

We propose a general framework for simultaneous segmentation and modelling of signals based on an Equipartition Principle (EP). According to EP, the signal is divided into segments with equal reconstruction errors by selecting the most suitable model to describe each segment. In addition, taking into account change detection on signal model an efficient signal reconstruction is also obtained. The model selection concerns both the kind and the order of the model. The proposed methodology is very flexible on different error criteria and signal features.

We present a key frames selection algorithm based on three iso-content principles (iso-content distance, iso-content error and iso-content distortion), so that the selected key frames are equidistant in video content according to the used principle. Two automatic approaches for defining the most appropriate number of key frames are proposed by exploiting supervised and unsupervised content criteria. Experimental results and the comparisons with existing methods from literature on large dataset of real-life video sequences illustrate the high performance of the proposed schemata.

We present a shape-based method for automatic people detection and counting without any assumption concerning camera motion. In order to evaluate the robustness of the proposed method, we apply it for classifying athletics videos into two classes: videos of individual and videos of team sports. The videos used are real and characterized by dynamic and unconstrained environment. Moreover, in the case of team sport, we propose a shape deformations based method for running/hurdling discrimination (activity recognition). Robust, adaptive and independent from color, illumination changes and the camera motion, the proposed features are combined in the Transferable Belief Model (TBM) framework providing a two-level (frames and shot) video categorization. Experimental results of 97% of accuracy for individual/team sport categorization using a dataset of 252 real videos of athletic meetings, acquired by moving cameras under varying view angles, indicate the stability and the good performance of the proposed scheme.

In this paper, we propose a method for the automatic identification of P-phase arrival based on the distribution of local maxima (LM) in earthquake seismograms. The method efficiently combines energy and frequency characteristics of the LM distribution (LMD). The detection is mainly based on the energy of a seismic event in the case the earthquake has higher amplitude than seismic background noise. Otherwise, it is based on the frequency of LM. Thus, the method provides robust detection of P-phase arrival in any quality type of seismic data. Moreover, it uses two sequential sliding signal windows yielding very high accuracy on the P-phase estimation. A hierarchical P-phase detection algorithm dramatically reduces the computational cost, making possible a real-time implementation. Experimental results from a large database of more than 80 low, medium, and high signal-to-noise ratio seismic events and comparison with existing methods in the literature indicate the reliable performance of the proposed scheme.

We propose a general framework that focuses on automatic individual/multiple people motion-shape analysis and on suitable features extraction that can be used on action/activity recognition problems under real, dynamical and unconstrained environments. We have considered various athletic videos from a single uncalibrated, possibly moving camera in order to evaluate the robustness of the proposed method. We have used an easily expanded hierarchical scheme in order to classify them to videos of individual and team sports. Robust, adaptive and independent from the camera motion, the proposed features are combined within Transferable Belief Model (TBM) framework providing a two level (frames and shot) video categorization. The experimental results of 97% individual/team sport categorization accuracy, using a dataset of more than 250 videos of athletic meetings indicate the good performance of the proposed scheme.

In this paper we present a video summarization scheme. First, shot detection is performed and then we extract the key frames under an equality requirement on subshots. We propose a key frames selection algorithm (Iso-Content MINMAX), which is very flexible on any choice of content descriptors, and is based on MINMAX optimization formulation. The equality principle provides to the selected key frames the useful property to be equivalent on video content summarization.

Over the last several years, major efforts have been made to develop methods for extracting information from audiovisual media, in order that they may be stored and retrieved in databases automatically, based on their content. In this work we deal with the characterization of an audio signal, which may be part of a larger audiovisual system or may be autonomous, as for example in the case of an audio recording stored digitally on disk. Our goal was to first develop a system for segmentation of the audio signal, and then classification into one of two main categories: speech or music. Among the system's requirements are its processing speed and its ability to function in a real-time environment with a small responding delay. Because of the restriction to two classes, the characteristics that are extracted are considerably reduced and moreover the required computations are straightforward. Experimental results show that efficiency is exceptionally good, without sacrificing performance. Segmentation is based on mean signal amplitude distribution, whereas classification utilizes an additional characteristic related to the frequency. The classification algorithm may be used either in conjunction with the segmentation algorithm, in which case it verifies or refutes a music-speech or speech-music change, or autonomously, with given audio segments. The basic characteristics are computed in 20 ms intervals, resulting in the segments' limits being specified within an accuracy of 20 ms. The smallest segment length is one second. The segmentation and classification algorithms were benchmarked on a large data set, with correct segmentation about 97% of the time and correct classification about 95%.