This paper proposes a novel social media relevance feedback algorithm, based on multimedia content power (MCP). The algorithm estimates in a recursive manner, the similarity measure. This is accomplished by using a set of relevant/irrelevant samples, which are provided by the user, in order to adjust the system's response. In particular, the similarity measure is expressed in a parametric form of functional components. Another innovative point has to do with the estimation of MCP, which measures the influence of files over social media users. Toward this direction, user interactions (e.g., comments, likes, and shares) indicate that the file is influencing to them. The algorithm takes into consideration both the visual characteristics of multimedia files and their influence to retrieve information. The experimental results show that the proposed scheme offers several merits and future work is also discussed.

Large volumes of video particles on practically every major sports event are posted on social media. According to socialbakers.com [1], four of the top twenty Facebook pages focus on sports. These particles can be processed and automatically annotated with events, entities etc. Furthermore, several annotated particles referring to a different time interval of the same sports event, could be synchronized to accomplish annotation of full sports games. Towards this direction, in this paper an innovative scheme is proposed that performs unsupervised annotation of sports video particles, posted on social media. The scheme is based on an intelligent wrapper architecture that automatically gathers and segments content and on the newly introduced Social Latent Semantic Analysis. This paper forms an initial study of automatic sports video particles annotation and experiments indicate its promising performance.

Social networks need to manage and control the drift of huge amounts of information by filtering and summarizing everything, in order to ensure they satisfy users' viewing pleasure. Until now social media content has already been used in a variety of applications such as for ranking of news stories, for profiling of user preferences, even for products' recommendations. However, this type of conversational, user-generated content might be used to add value to more traditional event media, such as video. In this paper we examine the capability of automatically producing meaningful summaries of generic videos. To do so we consider EdgeRank's affinity, weight and time decay parameters and implement a CLARANS-based key-frames extraction scheme. This paper forms an initial study of a social media video abstraction service and experiments indicate its promising performance.

We propose a novel method for automatic annotation, indexing and annotation-based retrieval of images. The new method, that we call Markovian Semantic Indexing (MSI), is presented in the context of an online image retrieval system. Assuming such a system, the users' queries are used to construct an Aggregate Markov Chain (AMC) through which the relevance between the keywords seen by the system is defined. The users' queries are also used to automatically annotate the images. A stochastic distance between images, based on their annotation and the keyword relevance captured in the AMC, is then introduced. Geometric interpretations of the proposed distance are provided and its relation to a clustering in the keyword space is investigated. By means of a new measure of Markovian state similarity, the mean first cross passage time (CPT), optimality properties of the proposed distance are proved. Images are modeled as points in a vector space and their similarity is measured with MSI. The new method is shown to possess certain theoretical advantages and also to achieve better Precision versus Recall results when compared to Latent Semantic Indexing (LSI) and probabilistic Latent Semantic Indexing (pLSI) methods in Annotation-Based Image Retrieval (ABIR) tasks.

An automatic video-object oriented steganographic system is proposed for biometrics authentication over error-prone networks. Initially, the host video object is automatically extracted through analysis of videoconference sequences. Next, the biometric pattern corresponding to the segmented video object is encrypted by a chaotic cipher module. Afterwards, the encrypted biometric signal is inserted to the most significant wavelet coefficients of the video object, using its qualified significant wavelet trees (QSWTs). QSWTs provide both invisibility and significant resistance against lossy transmission and compression, conditions that are typical in error prone networks. Finally, the inverse discrete wavelet transform (IDWT) is applied to provide the stego-object. Experimental results under various losses and JPEG compression ratios indicate the security, robustness, and efficiency of the proposed biometrics hiding system

In this article, a prototype Greek text to Greek Sign Language (GSL) conversion system is presented. The system is integrated into an educational platform that addresses the needs of teaching GSL grammar and was developed within the SYNENNOESE project (Efthimiou et al. 2004a Efthimiou, E. 2004a. “Developing an e-learning platform for the Greek sign language”. In Computer helping people with special needs, Lecture Notes in Computer Science Edited by: Miesenberger, K., Klaus, J. and Zagler, W. Vol. 3118, 1107–1113. [Crossref] , [Google Scholar]. Developing an e-learning platform for the Greek sign language. In: K. Miesenberger, J. Klaus, and W. Zagler, eds. Computer helping people with special needs, Lecture Notes in Computer Science, Springer-Verlag, vol. 3118, 1107–1113). The detailed implementation of the language-processing component of a Greek text to GSL conversion system is provided, focusing upon the inherent problems of knowledge elicitation of sign language (SL) grammar and its implementation within a parser framework. It is based on an SL dictionary (Efthimiou et al. 2004a Efthimiou, E. 2004a. “Developing an e-learning platform for the Greek sign language”. In Computer helping people with special needs, Lecture Notes in Computer Science Edited by: Miesenberger, K., Klaus, J. and Zagler, W. Vol. 3118, 1107–1113. [Crossref] , [Google Scholar]) database of coded GSL knowledge. The proposed system has been designed and implemented after considering most state-of-the-art SL machine translation or Conversion systems, such as Vsigns (Papadogiorgaki et al. 2004 Papadogiorgaki, M. VSigns – a virtual sign synthesis web tool. Proceedings of Sixth COST 276 Workshop on Information and Knowledge Management for Integrated Media Communication. May2004, Thessaloniki, Greece. [Google Scholar]. VSigns – a virtual sign synthesis web tool. In: Proceedings of Sixth COST 276 Workshop on Information and Knowledge Management for Integrated Media Communication, May 2004, Thessaloniki, Greece), ZARDOZ (Veale et al. 1998 Veale, T., Conway, A. and Collins, B. 1998. The challenges of cross-modal translation: English to sign language translation in the ZARDOZ system. Machine Translation, 13: 81–106. [Crossref] , [Google Scholar]. The challenges of cross-modal translation: English to sign language translation in the ZARDOZ system. Machine Translation, 13, 81–106) and SignSynth (Angus 2001 Angus, G. S.B. 2001. “SignSynth: a sign language synthesis application using Web3D and Perl”. In Gesture and Sign Language in Human–Computer Interaction, London, , UK: International Gesture Workshop. [Google Scholar]. SignSynth: a sign language synthesis application using Web3D and Perl. In: Gesture and Sign Lanauage in Human–Computer Interaction. London, UK: International Gesture Workshop), and taking into account their advantages and disadvantages. The overall architecture is innovative since other existing systems either do not consider the GSL or they cannot be effectively applied on sentences but just on single words. The system is demonstrable on any conventional PC.

In this paper, an affine invariant watermarking scheme, robust to geometric attacks, is proposed and applied to face regions. Initially, face regions are unsupervisedly extracted from an initial image and a normalization procedure, invariant to geometric attacks is applied on each of these regions using a set of specific moment criteria. A spread spectrum-based DS-CDMA watermarking scheme is then used in order to provide a multi bits watermark. The multi bits watermark embedding and detection procedures are then applicable to each normalized face region. Finally, performance of the proposed face regions watermarking scheme is tested under various distortions, providing efficient and robust watermark retrieval

The increasing popularity of multimedia applications creates an increasing need for secure storage and transmission techniques. Especially wireless communications, which can be easily intercepted, should be protected by eavesdroppers. Towards this direction, several encryption schemes have been proposed in literature, however most of them do not consider regions of interest (video objects). These regions may need better protection, may be the only regions that need protection, or should be accessed separately according to different privileges. For these reasons in this paper a video objects chaotic encryption system is proposed. Initially stereoscopic pairs are analyzed and video objects are automatically extracted using a color segments fusion approach. Next for each video object multiresolution decomposition is performed and the pixels of the lowest resolution level are encrypted using a chaotic cipher module. Finally the encrypted regions are propagated to the higher resolution levels and the encryption process is repeated until the highest level is reached. The system presents robustness against known cryptanalytic attacks, enables layered access of multimedia content and the overall security can be enhanced due to region topology.

In this paper an optimized and efficient technique for keyframes extraction of video sequences is proposed, which leads to selection of a meaningful set of video frames for each given shot. Initially for each frame, the singular value decomposition method is applied and a diagonal matrix is produced, containing the singular values of the frame. Afterwards, a feature vector is created for each frame, by gathering the respective singular values. Next, all feature vectors of the shot are collected to form the feature vectors basin of this shot. Finally, a genetic algorithm approach is proposed and applied to the vectors basin, for locating frames of minimally correlated feature vectors, which are selected as keyframes. Experimental results indicate the promising performance of the proposed scheme on real life video shots

Security of multimedia files attracts more and more attention and many encryption methods have been proposed in literature. However most cryptographic systems deal with multimedia files as binary large objects, without taking into consideration regions of semantic information. These regions may need better protection or can be the only regions that need protection, depending on the specific application. Towards this direction, in this paper we propose a human video object encryption system based on the chaotic logistic map. Initially face regions are efficiently detected and afterwards body regions are extracted, using geometric information of the location of face regions. Next the pixels of extracted human video objects are encrypted using an iterative cipher module, which is based on a feedback mechanism responsible for mixing the current encryption parameters with encrypted information of the previous step. The system presents robustness against known cryptanalytic attacks, and can save us a great amount of computational resources and time devoted for encrypting the whole contents of a multimedia file.

In this paper, an unsupervised video object (VO) segmentation and tracking algorithm is proposed based on an adaptable neural-network architecture. The proposed scheme comprises: 1) a VO tracking module and 2) an initial VO estimation module. Object tracking is handled as a classification problem and implemented through an adaptive network classifier, which provides better results compared to conventional motion-based tracking algorithms. Network adaptation is accomplished through an efficient and cost effective weight updating algorithm, providing a minimum degradation of the previous network knowledge and taking into account the current content conditions. A retraining set is constructed and used for this purpose based on initial VO estimation results. Two different scenarios are investigated. The first concerns extraction of human entities in video conferencing applications, while the second exploits depth information to identify generic VOs in stereoscopic video sequences. Human face/ body detection based on Gaussian distributions is accomplished in the first scenario, while segmentation fusion is obtained using color and depth information in the second scenario. A decision mechanism is also incorporated to detect time instances for weight updating. Experimental results and comparisons indicate the good performance of the proposed scheme even in sequences with complicated content (object bending, occlusion).

Two efficient unsupervised video object segmentation approaches are proposed and then extensively compared in terms of computational cost and quality of segmentation results. Both methods exploit depth information. In particular a depth segments map is initially estimated by analyzing a stereoscopic pair of frames and applying a segmentation algorithm. In the first, a "constrained fusion of color segments" (CFCS), video object segmentation is performed by fusion of color segments according to a depth similarity criterion. In the second approach, first, a dilated version of the boundary of each depth segment is produced and several feature points are estimated on this dilated boundary. Then, for each initial point, a normalized motion geometric space (MGS) is created which determines the only allowed path on to which the point can move. In the last step, each initial point moves on to its MGS and stops according to a weighted stop-function. Experiments on real life stereoscopic sequences are presented to exhibit the speed and accuracy of the proposed schemes.

A fully automatic system for embedding visually recognizable watermark patterns to video objects is proposed. The architecture consists of 3 main modules. In the first module, unsupervised video object extraction is performed, by analyzing stereoscopic pairs of frames. Then each video object is decomposed into three levels with ten subbands, using the discrete wavelet transform (DWT) and three pairs of subbands are formed (HL/sub 3/, HL/sub 2/), (LH/sub 3/, LH/sub 2/) and (HH/sub 3/, HH/sub 2/). Next qualified significant wavelet trees (QSWTs) are estimated for the specific pair of subbands that contains the highest energy content compared to the other two pairs. QSWTs are derived from the embedded zerotree wavelet (EZW) algorithm and they are high-energy coefficient paths within the selected pair of subbands. Finally, in the third module, visually recognizable watermark patterns are redundantly embedded to the coefficients of the highest energy QSWTs and the inverse DWT is applied to provide the watermarked video object. The performance of the proposed video object watermarking system is tested under various signal distortions such as JPEG lossy compression, sharpening, blurring and adding different types of noise. Experimental results on real life stereoscopic images are presented to indicate the efficiency and robustness of the proposed scheme.

In this paper, an optimal and efficient algorithm for video summarization is proposed by exploiting temporal variations of video visual content. In particular, the most characteristic frames/shots (key-frames/shots) are extracted by estimating appropriate points on the feature vector curve, which represent in an optimal way the corresponding trajectory. This is performed by minimizing the approximation error of the feature vector curve and the respective curve formed by the estimated points using an interpolation scheme. A genetic algorithm is used for the minimization task, since the complexity of an exhaustive search is too large to be implemented. Furthermore, a fast technique for increasing the number of extracted key-frames/shots is presented.

A wavelet-based steganographic method is proposed for robust message hiding. The message is embedded into the most significant wavelet coefficients of a cover image to provide invisibility and resistance against lossy transmission, compression or other distortion. The architecture consists of three modules. In the first module, the initial message is enciphered by an encryption algorithm. The enciphered message is imprinted onto a white-background image to construct the message-image to be hidden. In the second module, the cover image is decomposed into two levels with seven subbands, using the DWT. Next, qualified significant wavelet trees (QSWTs), which are paths of significant wavelet coefficients, are estimated for the highest energy pair of subbands. In the third module, the message-image is redundantly embedded to the coefficients of the best QSWTs and the IDWT is applied to provide the stego-image. The robustness and efficiency of the proposed steganographic system is evaluated under various loss rates, combined with different JPEG compression ratios.

In this paper constrained gradient vector flow (GVF) field generation is performed, for fast and accurate unsupervised stereoscopic semantic segmentation. The scheme utilizes the information provided by a depth segments map, produced by stereo analysis methods and incorporation of a segmentation algorithm. Then a Canny edge detector is applied to the depth region and produces an edge map. The edge map is used for tube estimation inside which the GVF field evolves. After generation of the GVF field an active contour is unsupervisedly initialized onto the outer bound of the tube. Finally a greedy approach is adopted and the active contour, guided by the GVF field, extracts the VOP. Experimental results on real life stereoscopic video sequences indicate the efficiency of the proposed scheme.

An efficient system for unsupervised structuring of stereoscopic sequences is presented. It generates links between similar VOPs (video object planes) of different shots. Particularly after shot cut detection, for each shot, a fast, unsupervised VOP detection and tracking algorithm is applied. Then for each of the foreground VOPs of a frame, a feature vector is constructed using low level features of the VOP, such as color and size. Afterwards, for a given shot, key-VOP poses are extracted for each VOP, using an optimization method for locating VOP poses of minimally correlated feature vectors. Finally an iterative process is performed to link each key VOP of a shot to another, according to a correlation measure. Experimental results indicate the promising performance of the proposed system on real life stereoscopic video sequences.

In this paper an efficient scheme for automatic organization of stereo-captured video sequences is presented, which exploits foreground VOP information of frames. More specifically after shot cut detection, for each frame of a shot, a fast, unsupervised foreground VOP extraction algorithm is applied, based on depth information and normalized Motion Geometric Spaces. Then for each frame, a feature vector is constructed, containing color and size characteristics of the foreground VOPs within the frame. Afterwards, for a given shot, key frames are extracted using an optimization method for locating minimally correlated feature vectors. Finally correlation links are generated between the extracted key frames to produce a graph-like structure of the video sequence. Experimental results on real life stereoscopic sequences indicate the promising performance of the proposed scheme.

In this paper, an efficient scheme for video object segmentation is proposed. The scheme is based on a multiresolution Gradient Vector Flow field (M-GVF) and a Motion Geometric Space (MGS) formulation. In particular the proposed scheme is initialized from an object approximation which can be provided either (a) automatically (unsupervised case) based on a depth map estimation method or (b) semi-automatically by user interaction. In the following, several feature points are estimated on the initial object contour (i.e. depth object) and an M-GVF adapted MGS is created to determine the direction that a feature point is allowed to move to. In this framework, each feature point moves onto its MGS in order to locate the contour of the physical video object. Experimental results are presented to indicate the reliable performance of the proposed scheme on real life stereoscopic and monocular video sequences.

Current multimedia databases contain a wealth of information in the form of audiovisual and text data. Even though efficient search algorithms have been developed for either media, there still exists the need for abstract data presentation and summarization. Moreover, retrieval systems should be capable of providing the user with additional information related to the specific subject of the query, as

A multiscale video content organization scheme is proposed for fast browsing and efficient transmission of video sequences. The scheme leads to construction of a five-layer tree structure. At layer 0 the root-node is located, connected to all nodes of layer 1, each corresponding to a class of shots. Then every class of shot is expanded at layer 2. The nodes of this layer represent shots. At the next resolution level (layer 3) nodes represent key-frames of shots. Finally at layer 4 the full resolution level is reached, where nodes correspond to frames of the sequence. Each node contains a viewing element and we focus on the extraction of these elements for layers 1, 2 and 3. Viewing elements of layers 1 and 3 are optimally extracted by minimizing a cross correlation criterion. Additionally viewing elements of layer 2 are selected according to a correlation measure between the mean vector of a shot and each of the frames within this shot. The resulting tree-structure enables a user to quickly and easily detect content of interest, by selecting the viewing element of his/her liking. Experimental results on real-life video sequences indicate the promising performance of the proposed scheme.

An efficient technique for summarization of stereoscopic video sequences is presented, which extracts a small but meaningful set of video frames using a content-based sampling algorithm. The proposed video-content representation provides the capability of browsing digital stereoscopic video sequences and performing more efficient content-based queries and indexing. Each stereoscopic video sequence is first partitioned into shots by applying a shot-cut detection algorithm so that frames (or stereo pairs) of similar visual characteristics are gathered together. Each shot is then analyzed using stereo-imaging techniques, and the disparity field, occluded areas, and depth map are estimated. A multiresolution implementation of the recursive shortest spanning tree (RSST) algorithm is applied for color and depth segmentation, while fusion of color and depth segments is employed for reliable video object extraction. In particular, color segments are projected onto depth segments so that video objects on the same depth plane are retained, while at the same time accurate object boundaries are extracted. Feature vectors are then constructed using multidimensional fuzzy classification of segment features including size, location, color, and depth. Shot selection is accomplished by clustering similar shots based on the generalized Lloyd-Max algorithm, while for a given shot, key frames are extracted using an optimization method for locating frames of minimally correlated feature vectors. For efficient implementation of the latter method, a genetic algorithm is used. Experimental results are presented, which indicate the reliable performance of the proposed scheme on real-life stereoscopic video sequences.