Waleed Farag

A human-based technique for measuring video data similarity

The increasing use of multimedia streams nowadays necessitates the development of efficient and effective methodologies for manipulating databases in storing them. Moreover, content-based access to multimedia databases requires in its retrieval stage to effectively asses the similarity of video data. This work proposes a new technique for measuring video data similarity that attempts to model some of the factors that reflect human notion in evaluating video data similarity. This model presents one step towards designing intelligent content-based video retrieval systems capable of measuring the similarity among video clips in a way similar to what humans do. The performance of the proposed model was tested where the system yielded very satisfactory values of recall and precision under various testing scenarios.

A new paradigm for analysis of MPEG compressed videos

The increasing use of multimedia streams nowadays necessitates the development of efficient and effective methodologies for manipulating databases storing these streams. Moreover, content-based access to multimedia databases requires in its first stage to parse the video stream into separate shots then apply a method to summarize the huge amount of data involved in each shot. This work proposes a new paradigm capable of robustly and effectively analyzing the compressed MPEG video data. First, an abstract representation of the compressed MPEG video stream is extracted and used as input to a neural network module (NNM) that performs the shot detection task. Second, we propose two adaptive algorithms to effectively select key frames from segmented video shots produced by the segmentation stage. Both algorithms apply a two-level adaptation mechanism in which the first level is based on the dimension of the input video file while the second level is performed on a shot-by-shot basis in order to account for the fact that different shots have different levels of activity. Experimental results show the efficiency and robustness of the proposed system in detecting shot boundaries and flashlights occurring within shots and in selecting the near optimal set of key frames (KFs) required to represent each shot.

The Essential Elements of Interactive Multimedia Distance Learning Systems

In recent years, we have seen the introduction and use of many distance learning systems. Some of these systems are characterized as interactive, which means the dominant mode of instruction is live or synchronous using networked multimedia technology such as audio, video and shared workspaces. In this paper, we present the common features and essential elements that should be implemented in such systems. Throughout this paper, we will use as a model and case study the IRI-h system (for Interactive Remote Instruction-heterogeneous) that we have developed and implemented in Java and have used to support distance learning at Old Dominion University.

Developing interactive classroom exercises for use with mobile devices to enhance class engagement and problem-solving skills

A recent Pew research center study of mobile device usage revealed that, African American and Latinos are the most active users of the Internet from mobile devices. The study also revealed that minority cell phone owners take advantage of a much greater range of their phones features compared with people of other ethnicities. At Winston Salem State University (WSSU), it is common for students to multitask and use their mobile devices while in class for studying, or performing other activities. This paper reports our ongoing experiences running a National Science Foundation (NSF)-sponsored targeted Infusion Project (TIP) in Computer Science Department that aims to leverage this situation by developing a mobile classroom response system (MRS) to allow students solve interactive problems in their mobile devices in order to improve their class engagement and problem solving skills. By allowing them to solve problems in their preferred devices, the project expects to create a friendly learning environment where the students want to retain, be active and skillful.

Wavelet-based polarimetry analysis

Wavelet transformation has become a cutting edge and promising approach in the field of image and signal processing. A wavelet is a waveform of effectively limited duration that has an average value of zero. Wavelet analysis is done by breaking up the signal into shifted and scaled versions of the original signal. The key advantage of a wavelet is that it is capable of revealing smaller changes, trends, and breakdown points that are not revealed by other techniques such as Fourier analysis. The phenomenon of polarization has been studied for quite some time and is a very useful tool for target detection and tracking. Long Wave Infrared (LWIR) polarization is beneficial for detecting camouflaged objects and is a useful approach when identifying and distinguishing manmade objects from natural clutter. In addition, the Stokes Polarization Parameters, which are calculated from 0°, 45°, 90°, 135° right circular, and left circular intensity measurements, provide spatial orientations of target features and suppress natural features. In this paper, we propose a wavelet-based polarimetry analysis (WPA) method to analyze Long Wave Infrared Polarimetry Imagery to discriminate targets such as dismounts and vehicles from background clutter. These parameters can be used for image thresholding and segmentation. Experimental results show the wavelet-based polarimetry analysis is efficient and can be used in a wide range of applications such as change detection, shape extraction, target recognition, and feature-aided tracking.

Assessing Digital Video Data Similarity

Multimedia applications are rapidly spread at an everincreasing rate, introducing a number of challenging problems at the hands of the research community. The most significant and influential problem among them is the effective access to stored data. In spite of the popularity of keyword-based search technique in alphanumeric databases, it is inadequate for use with multimedia data due to their unstructured nature. On the other hand, a number of video content and contextbased access techniques have been developed (Deb, 2005). The basic idea of content-based retrieval is to access multimedia data by their contents, for example, using one of the visual content features. While context-based techniques try to improve the retrieval performance by using associated contextual information, other than those derived from the media content (Hori & Aizawa, 2003). Most of the proposed video indexing and retrieval prototypes have two major phases, the database population and the retrieval phase. In the former one, the video stream is partitioned into its constituent shots in a process known as shot boundary detection (Farag & Abdel-Wahab, 2001, 2002b). This step is followed by a process of selecting representative frames to summarize video shots (Farag & Abdel-Wahab, 2002a). Then, a number of low-level features (color, texture, object motion, etc.) are extracted in order to use them as indices to shots. The database population phase is performed as an off-line activity and it outputs a set of metadata with each element representing one of the clips in the video archive. In the retrieval phase, a query is presented to the system that in turns performs similarity matching operations and returns similar data back to the user. The basic objective of an automated video retrieval system (described above) is to provide the user with easy-to-use and effective mechanisms to access the required information. For that reason, the success of a content-based video access system is mainly measured by the effectiveness of its retrieval phase. The general query model adopted by almost all multimedia retrieval systems is the QBE (query by example; Marchionini, 2006). In this model, the user submits a query in the form of an image or a video clip (in case of a video retrieval system) and asks the system to retrieve similar data. QBE is considered to be a promising technique since it provides the user with an intuitive way of query presentation. In addition, the form of expressing a query condition is close to that of the data to be evaluated. Upon the reception of the submitted query, the retrieval stage analyzes it to extract a set of features then performs the task of similarity matching. In the latter task, the query-extracted features are compared with the features stored into the metadata; then matches are sorted and displayed back to the user based on how close a hit is to the input query. A central issue here is the assessment of video data similarity. Appropriately answering the following questions has a crucial impact on the effectiveness and applicability of the retrieval system. How are the similarity matching operations performed and based on what criteria? Do the employed similarity matching models reflect the human perception of multimedia similarity? The main focus of this article is to shed the light on possible answers to the above questions.