Video-Based Learning (VBL) has a long history in the educational design research. In the past decade, the interest in VBL has increased as a result of new forms of online education, such as flipped classrooms, and most prominently MOOCs. VBL has unique features that make it an effective Technology-Enhanced Learning (TEL) approach. This study critically analyzed the current research of VBL published in 2003-2013 to build a deep understanding on what are the educational benefits and effectiveness that VBL has on teaching and learning. 67 peer reviewed papers were selected in this review and categorized into four main dimensions, namely, effectiveness, teaching methods, design, and reflection. In the light of the discussion of current research in terms of these categories, we present the future vision and research opportunities of VBL that support self-organized and network learning.

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Video-Based Learning: A Critical Analysis of The Research Published in 2003-2013 and Future Visions

Information retrieval systems are getting more attention in the era of multimedia technologies such as an image, video, audio and text files. The large numbers of images are challenges in computer systems field to store, manage data effectively and efficiently. The shape retrieval feature of different objects in the image also remains a difficult problem due to distinct angle view of different objects in a scene only; few studies have reported solution to the problem of finding relative locations of ROIs. In this paper, we proposed three methods such as1. Geolocation-based image retrieval (GLBIR), 2.Unsupervised feature technique Principal component analysis (PCA) and 3.multiple region-based image retrieval. The first proposed (GLBIR) method identifies geo location an image using visual attention based mechanism and its color layout descriptors. These features are extracted from geo-location of query image from Flickr database. Our proposed model does not fully semantic understanding of image content, uses visual metrics for example; the proximity ,color contrast, size and nearness to image's boundaries to locate viewer's attention. We analyzed results and compared with state of art CBIR Systems and GLBIR Technique. Our second method to refine images exploiting and fusing by unsupervised feature technique using principal component analysis (PCA). The visually similar images clustering together with analyses image retrieval process and remove outliers initially retrieved image set by PCA. To evaluation our proposed approach, we used thousands of images downloaded from Flickr and CIFAR-10 databases using Flickr public API. Finally, we determinately proposed a system for image retrieval based on region. It provides a user interface for availing to designate the watershed ROI within an input image. During the retrieval of images, regions' feature vectors having codes of region homogeneous to a region of input image are utilized for comparison. Standard datasets are used for evaluation of proposed approach. The experiment demonstrates and effectiveness of the proposed method to achieve higher annotation performance increases accuracy and reduces image retrieval time. We evaluated our proposed approach on images dataset from Flickr and CIFAR-10.

GEO matching regions: multiple regions of interests using content based image retrieval based on relative locations

The pedagogical strength of Video-Based Learning (VBL) is presenting knowledge in consistent and attractive manner. In recent years, the new forms and technologies of VBL such as flipped classrooms, and most prominently MOOCs, have had a remarkable impact on teaching and learning methodologies. A significant number of academic publications have investigated and analyzed VBL environments from different perspectives, including potential usage, effects on learning outcomes, satisfaction levels, and effectiveness. This study provides a critical analysis of the current research in VBL conducted from 2003 until today. We aim to help educators in building deeper understanding about the educational benefits of VBL. In this study, 76 peer reviewed papers are identified through journals and academic databases and they are categorized into four main dimensions: effectiveness, teaching methods, design, and reflection. In the scope of this analysis, we also provide future visions and research opportunities in VBL that support self-organized and network learning. Keywords-Video-Based Learning; VBL; MOOC; Review of Research; Blended Learning; Video Design; Flipped Classroom; Technology-Enhanced Learning.

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The State of Video-Based Learning: A Review and Future Perspectives

We propose AccountTrade, a set of accountable protocols, for big data trading among dishonest consumers. To secure the big data trading environment, our protocols achieve book-keeping ability and accountability against dishonest consumers who may misbehave throughout the dataset transactions. Specifically, we study the responsibilities of the consumers in the dataset trading and design AccountTrade to achieve accountability against the dishonest consumers who may try to deviate from their responsibilities. Specifically, we propose uniqueness index, a new rigorous measurement of the data uniqueness, as well as several accountable trading protocols to enable data brokers to blame the dishonest consumer when misbehavior is detected. We formally define, prove, and evaluate the accountability of our protocols by an automatic verification tool as well as extensive evaluation in real-world datasets. Our evaluation shows that AccountTrade incurs negligible constant storage overhead per file (<10KB), and it is able to handle 8–1000 concurrent data uploading per server depending on the data types.

AccountTrade: Accountable protocols for big data trading against dishonest consumers

Over the past few years there has been an increasing interest to investigate the potential of Video-Based Learning (VBL) as a result of new forms of online education, such as flipped classrooms and Massive Open Online Courses (MOOCs) in order to engage learners in a self-organized and networked learning experience. However, current VBL approaches suffer from several limitations. These include the focus on the traditional teacher-centered model, the lack of human interaction, the lack of interactivity around the video content, lack of personalization, as well as assessment and feedback. In this paper, we investigate the effective design of VBL environments and present the design, implementation, and evaluation details of CourseMapper as a mind map-based collaborative video annotation and analytics platform that enables learners’ collaboration and interaction around a video lecture. Thereby, we focus on the application of learning analytics mainly from a learner perspective to support self-organized and networked learning through personalization of the learning environment, monitoring of the learning process, awareness, self-reflection, motivation, and feedback.

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Video annotation and analytics in CourseMapper

The modern world is enclosed with gigantic masses of digital visual information. Increase in the images has urged for the development of robust and efficient object recognition techniques. Most work reported in the literature focuses on competent techniques for object recognition and its applications. A single object can be easily detected in an image. Multiple objects in an image can be detected by using different object detectors simultaneously. The paper discusses various techniques for object recognition and a method for multiple object detection in an image. The modern world is enclosed with gigantic masses of digital visual information. To analyze and organize these devastating ocean of visual information image analysis techniques are major requisite. In particular useful would be methods that could automatically analyze the semantic contents of images or videos. The content of the image determines the significance in most of the potential uses. One important aspect of image content is the objects in the image. So there is a need for object recognition techniques. Object recognition is an important task in image processing and computer vision. It is concerned with determining the identity of an object being observed in an image from a set of known tags. Humans can recognize any object in the real world easily without any efforts; on contrary machines by itself cannot recognize objects. Algorithmic descriptions of recognition task are implemented on machines; which is an intricate task. Thus object recognition techniques need to be developed which are less complex and efficient. Many successful approaches that address the problem of general object detection use a representation of the image objects by a collection of local descriptors of the image content. Global features provide better recognition. Color and shape features can also be used. Various object recognition techniques are presented in this paper. Difficulties may arise during the process of object recognition; a range of such difficulties are discussed in this paper. The robust and efficient object recognition technique can be developed by

Techniques for Object Recognition in Images and Multi-Object Detection

Image annotation is an active field of research that serves as a precursor to video annotation. With the increase in the number of videos and important information present in them, there is need to annotate the videos. Annotation improves the efficiency of searching and retrieving the video. Video features are often inspired and sometimes directly borrowed from image techniques and many methods for image indexing are also easily applied to video. A human operator can specify annotations such as time, location and activity. More sophisticated annotations can be provided using automatic or semi-automatic techniques. There are various techniques that can be used for annotation; this paper discusses some of the techniques. Technique which employs the ontology, requires ontology language, web ontology language (OWL) is the ontology language that has gained importance. This paper also discusses ontology and knowledge base editor- Protege.

Study of Various Video Annotation Techniques

Recent advances in technology have made tremendous amount of multimedia content available. The amount of video content is increasing, due to which the systems that improve the access to the video is needed. This can be done by annotation of video, which facilitate the faster access to the videos. The first step towards the video annotation is the extraction of key frames. Instead of analysing all the frames in the video, only the frames which contain important information of the video can be used for further processing. In this paper, key frame extraction method is discussed which assist the video annotation process. The key frames are found by computing the edge difference between the consecutive frames and those frames exceeding the threshold are considered as key frames.

Key frame extraction methodology for video annotation

Increase in the importance of video data has led to flooding of video content over the internet and offline world. For such abundance of content the access of relevant video in reduced time is a matter of concern. So the need for a system which will assist in content based access of video arises. This paper introduces a novel approach for video annotation. The key frames are extracted from the video and are analyzed. Instead of complete video frames, only the key frames are analyzed to identify the objects present. The object detectors are trained for identification of the object. The detected objects are then added in the annotation file. The annotation is based on ontology which eases the semantic retrieval of videos. Ontology based video annotation greatly accelerates the performance of retrieval systems.

Video Annotation Methodology Based on Ontology for Transportation Domain

Large and continuously growing dynamic web content has created new opportunities for large-scale data analysis in the recent years. There is huge amount of information that the traditional web crawlers cannot access, since they use link analysis technique by which only the surface web can be accessed. Traditional search engine crawlers require the web pages to be linked to other pages via hyperlinks causing large amount of web data to be hidden from the crawlers. Enormous data is available in deep web that can be useful to gain new insight for various domains, creating need to access the information from the deep web by developing efficient techniques. As the amount of Web content grows rapidly, the types of data sources are proliferating, which often provide heterogeneous data. So we need to select Deep Web Data sources that can be used by the integration systems. The paper discusses various techniques that can be used to surface the deep web information and techniques for Deep Web Source Selection.

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Khushboo Khurana

Papers

Techniques for Object Recognition in Images and Multi-Object Detection

Study of Various Video Annotation Techniques

Key frame extraction methodology for video annotation

Video Annotation Methodology Based on Ontology for Transportation Domain

Survey of Techniques for Deep Web Source Selection and Surfacing the Hidden Web Content