Overlapped fingerprints can be potentially present in several civil applications and criminal investigations. Segmentation of overlapped fingerprints is a required step in the process of fingerprint separation and subsequent verification. Overlapped fingerprint segmentation is performed manually (and the resulting manually drawn masks are a required additional input) in all of the overlapped latent fingerprints separation approaches in the literature, which make them only semi‐automatic. This study proposes a novel overlapped fingerprint mask segmentation approach, thereby filling that gap in the development of fully automated fingerprint separation solutions. The proposed method uses convolutional neural networks to classify image blocks into three classes – background, single region, and overlapped region. The proposed approach shows satisfactory performance on three different datasets and opens the door for full automation of fingerprint separation algorithms, which is a very promising research area.

This work extends PneumoCAD, a Computer-Aided Diagnosis system for detecting pneumonia in infants using radiographic images, with the aim of improving the system's accuracy and robustness. We implement and compare five con-temporary machine learning classifiers, namely: Naïve Bayes, K-Nearest Neighbor (KNN), Support Vector Machines (SVM), Multi-Layer Perceptron (MLP) and Decision Tree, combined with three dimensionality reduction algorithms: Sequential Forward Selection (SFS), Principal Component Analysis (PCA) and Kernel Principal Component Analysis (KPCA). Current results demonstrate that Naïve Bayes classifier combined with KPCA produces the best overall results.

Due to the rapid development of information technology and the continuously increasing number of available multimedia data, the task of retrieving information based on visual content has become a popular subject of scientific interest. Recent approaches adopt the bag-of-visual-words (BOVW) model to retrieve images in a semantic way. BOVW has shown remarkable performance in content-based image retrieval tasks, exhibiting better retrieval effectiveness over global and local feature (LF) representations. The performance of the BOVW approach depends strongly, however, on predicting the ideal codebook size, a difficult and database-dependent task. The contribution of this paper is threefold. First, it presents a new technique that uses a self-growing and self-organized neural gas network to calculate the most appropriate size of a codebook for a given database. Second, it proposes a new soft-weighting technique, whereby each LF is classified into only one visual word (VW) with a degree of participation. Third, by combining the information derived from the method that automatically detects the number of VWs, the soft-weighting method, and a color information extraction method from the literature, it shapes a new descriptor, called color VWs. Experimental results on two well-known benchmarking databases demonstrate that the proposed descriptor outperforms 15 contemporary descriptors and methods from the literature, in terms of both precision at K and its ability to retrieve the entire ground truth.

Many photo-taking scenarios have emotional triggers such as happiness, joy, or in general a "good feeling". In this paper we postulate that if a photo was taken to preserve a good feeling, it will most likely trigger a good feeling when re-visiting the photo later on. We present an approach to classify photos and decide automatically whether they have been taken to preserve a good feeling or not. We employ an annotated data set to create a model and apply this model to enhance classical image search. The result is a novel search approach, demonstrated by a proof-of-concept application capable of performing keyword-based search and filtering out the results that are considered unlikely to convey a good feeling to the viewer.

Visual information retrieval (VIR) is an active and vibrant research area, which attempts at providing means for organizing, indexing, annotating, and retrieving visual information (images and videos) form large, unstructured repositories. The goal of VIR is to retrieve the highest number of relevant matches to a given query (often expressed as an example image and/or a series of keywords). In its early years (1995-2000) the research efforts were dominated by content-based approaches contributed primarily by the image and video processing community. During the past decade, it was widely recognized that the challenges imposed by the semantic gap (the lack of coincidence between an image's visual contents and its semantic interpretation) required a clever use of textual metadata (in addition to information extracted from the image's pixel contents) to make image and video retrieval solutions efficient and effective. The need to bridge (or at least narrow) the semantic gap has been one of the driving forces behind current VIR research. Additionally, other related research problems and market opportunities have started to emerge, offering a broad range of exciting problems for computer scientists and engineers to work on. In this tutorial, we present an overview of visual information retrieval (VIR) concepts, techniques, algorithms, and applications. Several topics are supported by examples written in Java, using Lucene (an open-source Java-based indexing and search implementation) and LIRE (Lucene Image REtrieval), an open-source Java-based library for content-based image retrieval (CBIR) written by Mathias Lux.

Methods of retrieving images that incorporate human- generated metadata, such as keyword annotation and collaborative filtering, are less vulnerable to the semantic gap than content-based image retrieval. However, generating such metadata is time-consuming, expensive, and difficult to evaluate. This paper discusses an interface for image retrieval that is able to simultaneously compose queries based on content- based features, keywords, and collaborative relations. We propose using a game metaphor to evaluate the system.

A novel encryption model for digital videos is presented. The model relies on the encryption-compression duality of certain types of permutations acting on video frames. In essence, the proposed encryption process preserves the spatial correlation and, as such, can be applied prior to the compression stage of a spatial-only video encoder. Several algorithmic modes of the proposed model targeted for different application requirements are presented and analyzed in terms of security and performance. Experimental results are generated for a number of standard benchmark sequences showing that the proposed method, in addition to providing confidentiality, preserves or improves the compression ratio. 1

Purpose The purpose is to enhance the capabilities of a general‐purpose IDS solution with additional knowledge of multimedia file formats and protocols, to better handle multimedia‐specific security exploits. Design/methodology/approach The authors have designed a multimedia traffic classifier, implemented as an optional preprocessor for Snort. The solution has been successfully tested with downloading and streaming traffic. Findings Test results confirm that the additional specialized knowledge encoded in the preprocessor results in two significant gains: trusted multimedia contents can be identified and allowed to bypass the detection engine, with substantial computational savings; the IDS is now able to detect multimedia‐specific exploits which would otherwise go unnoticed. Research limitations/implications Not all multimedia‐related scenarios have been covered by the described implementation yet. The proposed solution is being extended to other file types and protocols, fine‐tuned, as well as tested more extensively. Practical implications Snort users interested in this work will be able to add the multimedia‐specific functionality – and enjoy the resulting benefits – with minimal effort. Originality/value The research reported in this paper is – to the authors' knowledge – the first effort to add multimedia‐specific knowledge to the operation of an IDS. In addition to being innovative, the proposed method is relevant for more than one reason, since it enhances the IDS capabilities while at the same time alleviating the computational cost of performing detailed traffic analysis in high‐speed networks.

This paper presents a novel background modeling and subtraction approach for video object segmentation. A neural network (NN) architecture is proposed to form an unsupervised Bayesian classifier for this application domain. The constructed classifier efficiently handles the segmentation in natural-scene sequences with complex background motion and changes in illumination. The weights of the proposed NN serve as a model of the background and are temporally updated to reflect the observed statistics of background. The segmentation performance of the proposed NN is qualitatively and quantitatively examined and compared to two extant probabilistic object segmentation algorithms, based on a previously published test pool containing diverse surveillance-related sequences. The proposed algorithm is parallelized on a subpixel level and designed to enable efficient hardware implementation.

This paper explores the challenges opportunities in developing and deploying 3D TV services. The 3D TV services can be seen as a general case of the multi-view video that has been receiving significant attention lately. The keys to a successful 3D TV experience are the availability of content, the ease of use, the quality of experience, and the cost of deployment. Recent technological advances have made possible experimental systems that can be used to evaluate the 3D TV services. We have developed a 3D TV prototype and have currently conducting our first user study to evaluate the quality and experience. These experiences have allowed us to identify challenges and opportunities in developing 3D TV services

Content-based image retrieval (CBIR) systems have been actively investigated over the past decade. Several existing CBIR prototypes claim to be designed based on perceptual characteristics of the human visual system, but even those who do are far from recognizing that they could benefit further by incorporating ongoing research in vision science. This paper explores the inclusion of human visual perception knowledge into the design and implementation of CBIR systems. Particularly, it addresses the latest developments in computational modeling of human visual attention. This fresh way of revisiting concepts in CBIR based on the latest findings and open questions in vision science research has the potential to overcome some of the challenges faced by CBIR systems

Low bitrates enabled by the H.264/AVC standard come at the cost of significant decoding complexity. The problem is exacerbated when multi-view video coding (MVC), presently being standardized by the MPEG committee, is considered. Increasing the number of reference views will improve the prediction but also increase the encoding and decoding complexity. In this paper we present an alternative to transform coding and arithmetic coding used to code motion compensation residuals in H.264/AVC. Proposed approach is sorting permutation based, resulting in significantly decreased decoding complexity. We evaluate the efficiency of the coding and present initial results obtained for some sequences in published literature

This demonstration highlights the benefits that image retrieval systems can enjoy by use of a thoughtful interface. We present a live demonstration of PRISM, a new Web-based application that, through a simple set of natural actions, allows the user the ability to elicit sophisticated responses from easily-formed queries. Additionally, the system was designed with not only content-based, but also content-free image retrieval and semantic annotation in mind. The demonstration system uses an attention-based method for extracting and determining the relevance of salient regions of interest in an unsupervised way, but can be adapted to a variety of implementations

We have developed a biologically-motivated, unsupervised way of grouping together images whose salient regions of interest (ROIs) are perceptually similar regardless of the visual contents of other (less relevant) parts of the image. In the implemented model cluster membership is assigned based on feature vectors extracted from salient ROIs. This paper focuses on the experimental evaluation of the proposed approach for several combinations of feature extraction techniques and unsupervised clustering algorithms. The results reported here show that this is a valid approach and encourage further research.

There is a renewed interest in the 3DTV research primarily due to the advances in low cost 3D display technologies. The two views required for 3DTV can be compressed using standard video compression techniques. MPEG-2 is widely used in digital TV applications today and H.264/MPEG-4 AVC is expected to be the leading video technology standard for digital video in the near future. The compression gains and quality of 3DTV will vary depending on the video coding standard used. While inter-view prediction will likely improve the compression efficiency, new approaches such as asymmetric view coding are necessary to greatly reduce bandwidth requirements for 3DTV. This demonstration will show the quality of 3D video experience on autostereoscopic displays using H.264 and MPEG-2. We will also show the benefits of using asymmetric view coding as well as the role of eye dominance on 3DTV experience.

Recent advances in video compression and 3D displays have necessitated a further understanding and development of 3D video coding algorithms. The emergence of low cost autostereoscopic displays is expected to drive the growth of 3DTV services. This paper discusses key issues that affect the quality of 3D video experience on autostereoscopic displays. The characteristics of the human visual system can be exploited to compress individual stereo views at different qualities without affecting the perceptual quality of the 3D video. The H.264/AVC video coding algorithm was used to compress each view. We examine the bounds of asymmetric stereo view compression and its relationship to eye-dominance based on a user study. This paper also presents the design and development of a modular video player with stereoscopic and multi-view capabilities including a discussion of useful tools for accelerating the development and enhancing flexibility. The experimental results indicate that eye-dominance influences 3D perception and as a result will impact the coding efficiency of 3D video.

The temporal limitations of the human visual system (HVS) are usually not fully explored in the quantization stage of today’s video encoders. In this paper we present a novel method to improve the compression efficiency of popular video compression algorithms in which the temporal frequency response characteristics of the HVS are taken into account while building macroblockspecific quantization matrices for non-intra-coded (i.e., ‘P’ or ‘B’) frames. These quantization matrices help removing redundant information from the video sequence and, consequently, increase compression efficiency. Results from our experiments show that the proposed approach can achieve improved bit savings over a standard encoder without sacrificing the perceived visual quality of the compressed sequence.