Multimedia Representation

Today’s digital world is filled with a vast multitude of content such as text and multimedia. Various attempts are being made to develop modern and powerful search engines in order to support diverse queries on this large collection of textual and multimedia data. For supporting intelligent search, particularly for multimedia data such as images, additional metadata plays a crucial role in helping a search engine handpick the most relevant information for a query. A common technique that is used to generate pertinent metadata for visual multimedia content is by the process of annotation. Automating the annotation process given the large volume of visual content available on the Web is highly advantageous. In this paper, we propose an automated image annotation system that employs a content-based visual information retrieval technique using certain features of the image. Experimental evaluation and analysis of the proposed work have shown promising results.

In this paper, we propose a multimedia data management framework using GeM-Tree. GeM-Tree is a multidimensional tree-based index structure which provides a generalized framework to organize and retrieve images and videos seamlessly. In addition to supporting different multimedia data types and diverse representations, the proposed data management framework supports varied multimedia retrieval strategies like content-based image and video retrieval, mixed multimedia data retrieval where cross-similarity between images and videos is considered, region-based retrieval, etc. The framework embeds a high-level semantic relationship between multimedia data objects with a construct called Hierarchical Markov Model Mediator via a novel affinity promotion technique to improve query result relevance manifold. Extensive experiments were conducted with different multimedia data types possessing varied representations, different retrieval approaches, and different data sizes. The encouraging results demonstrate a potential solution to the important and complex issue of managing a large volume of multimedia data.

With rapid advances in storage devices, networks, and compression techniques, large‐scale multimedia data has become available to average users. How to index and search multimedia data according to its real content is a challenging problem, which has been studied for decades. Since the 1990s, content‐based multimedia retrieval (CBMR) had become an increasingly active field, which is defined as searching for desired multimedia data (images or video/audio segments/clips) relevant with issued queries, such as image/audio/video examples, keywords, phrases, sentences, or any combination of them.Different from text retrieval, CBMR is a more challenging task, as certain understanding of the content of multimedia data. It mainly involves two basic problems. One problem is how to represent queries and multimedia content. The other problem is how to map the representations of queries and multimedia content. Extensive works on CBMR have already been published, and different paradigms and techniques have been proposed, such as query‐by‐example (QBE), annotation‐based retrieval (ABR), and multimodality retrieval. Here we conclude that they are all proposed aiming at addressing the above two issues. QBE is the most typical scenario for multimedia retrieval before the year of 2000, whereas query by text and by combination of text and examples have become two new mainstream scenarios thereafter. In this article, we will introduce both classic CBMR and the current mainstream.

In response to the growing demand for educated professionals focusing on multimedia related industry, the University of Rostock developed a new specialisation in media technology in 1995. Although the engineering department at the university has long conducted research regarding the organisation, processing, transmission, and representation of multimedia data, these issues are now formally covered in lectures and coursework offered by various faculty within the engineering department.KeywordsMedium TechnologyMultimedia DataEngineering DepartmentInformation ElectronicsMedia StreamThese keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Data warehousing has drawn attention as a useful approach to integrate heterogeneous data sources. Since most of data warehouses have been developed based on the relational database technology, however, difficulties are encountered, when we integrate multimedia data sources, which need a flexible data model and a content-based query language. In this paper, we study a framework for multimedia data warehousing, which integrates diverse types of multimedia data from disparate sources. Because of the interoperability and flexibility of the XML, we use the XML technology for our framework. The multimedia data warehouse supports content-based integration and retrieval of multimedia data, and manages changes of data sources efficiently in a distributed environment. Therefore, our framework significantly simplifies development of Web-based advanced multimedia application.

A simulated annealing approach for multimedia data placement

With the increasing interest in the deployment of wireless multimedia sensor networks (WMSNs), new challenges arouse with effective use of camera sensors to provide maximized event coverage with the least amount of redundancy in the collected multimedia data. Given that the processing and transmission of multimedia data are costly in terms of energy, camera sensors should only be actuated when an event is detected within their vicinity. While achieving maximum coverage with such actuation is desirable, multiple camera sensors' field-of-view (FoV) can be covering the same spots and thus redundant multimedia data can unnecessarily be sent to the base-station. In this paper, assuming camera sensors with fixed orientation, we propose a low-cost distributed actuation scheme which strives to turn on the least number of camera sensors to avoid possible redundancy in the multimedia data while still providing the necessary event coverage. The basic idea of this distributed scheme is the collaboration of camera sensors that have heard from scalar sensors about an occurring event in order to minimize the possible coverage overlaps among their FoVs. The scheme requires only 1-hop information for camera sensors and its messaging overhead is negligible. Through simulation, we show how the distributed scheme performs with respect to the cases when all the cameras within the vicinity or the region are actuated and assess the performance under various conditions.

It is our great pleasure to welcome you to the 1st ACM International Workshop on Multimedia Analysis for Ecological Data (MAED'12) held within ACM Multimedia 2012, in Nara, Japan. MAED'12 brings together a cross-disciplinary crowd of people in order to investigate current and emerging topics within ecological multimedia data analysis. The workshop, in particular, outlines the state of the research on the most recent methods for the processing and interpretation of multimedia data recorded for monitoring ecological systems. In total, the Program Committee accepted 6 long and 6 short papers covering a variety of topics: Animal identification and behaviour understanding by mining image and video data; plant identification and classification on still images; classification and characterization of environmental habitats; multimedia data processing for pollution monitoring and ecological multimedia data retrieval. In addition, Prof. Alan Smeaton from the School of Computing of Dublin City University delivers the workshop's keynote entitled "Multimedia Challenges in Sensing the Environment".

Many of the database research issues involved in dealing with multimedia data are similar to those that arise in handling other nontraditional data such as spatial, image, temporal, text, document, and scientific. We focus here on multimedia database issues, which we hope can serve as a starting point for a wider discussion. Our examples are often taken from the spatial domain as this is where we have the greatest expertise, although these issues are far more general. Why do we want a database? The natural and simple answer is to be able to store and retrieve data efficiently. Notice the emphasis on retrieval. We should not lose sight of this purpose. For example, it means that storing images in long fields in a relational database is usually not the answer. Long fields are usually a stopgap solution as they are just a repository for data and do not aid in its retrieval. In particular, as the data volume gets large, this solution breaks down because the tuples get too large. We need to be able to integrate nontraditional data with traditional (e.g., alphanumeric) data. Alphanumeric data can frequently be treated just like locational data in that the records that make up the alphanumeric data are like points in a higher-dimensional space where each attribute is analogous to a spatial dimension. The difference is that spatial data have more than just a locational component. In particular, spatial data are distinguished from nonspatial data by having spatial extent. A number of attempts at integration take advantage of this analogy. However, it can also act as a straitjacket in the case of the relational model. Some examples of successful integration include spatial with nonspatial data [Aref and Samet 1990], temporal with nontemporal data including spatial data [Hjaltason and Samet 1995], document with nondocument data [SacksDavis et al. 1995], image with locational and nonvocational data [Samet and Soffer 1995], and so on. Efficient retrieval is facilitated by building an index [Samet 1990a, 1990b]. This means that we need to find a way to sort the data. Surprisingly, this is not always done for such applications (e.g., it is absent in the Photobook image database system [Pentland et al. 1994]). The index should be compatible with the data that are being stored, and we also need to choose an appropriate zero or reference point for it. The index should be implicit rather than explicit, as it is impossible to foresee all possible queries in advance. For example, in the spatial domain, assuming a relational model, it is impractical to have an attribute for each spatial relationship (e.g., north, northeast, left, etc.). Instead, the index should enable us to derive these relationships on the fly. As a more concrete example, an explicit index would sort twodimensional locational data on the basis of distance from a given point x; yet this would not be very useful if we wanted to have the locations sorted with respect to a different point y. In particular, we

Multimedia applications that treat various types of data like videos, audios, and images are becoming popular amongst the Internet users. Assuming best-effort networks as the infrastructure, we are developing a multimedia data retrieval system based on QoS (quality of service) concept that searches and retrieves various types of multimedia data from plural multimedia data archives. As one of QoS parameters, we select the user's response time. Since the response time is related with network throughputs, throughput measurement becomes an important technology in the system. So far, we have developed a mechanism to decide a retrieval order of multimedia data that match with the user's searching request. The mechanism is called QoS scenario and it balances the user's searching request (keyword matching) and the QoS factor related with the user's response time that can be determined by the throughput measurement. The QoS scenario was used in the first version of our multimedia data retrieval system. In this paper, we study the active and passive throughput measurement technologies. Also, a multimedia data structure is introduced on the basis of the Dublin Core that has been standardized. Owing to the multimedia data structure, the system is able to hold interoperability and wide-area-network search and retrieval can be realized.

Transport Protocol for Real-Time Multimedia Communication

Nowadays, security of valuable and secret assets is very important for large organization. Need of an efficient and reliable security system arises due to limitation of human resources and manpower. In such systems, video surveillance systems have their own significance. Although some people don’t like the idea of being monitored, surveillance systems improve the public security, allowing the system to detect dangers. Multimedia data typically means digital imagesaudio, video, animation and graphics together with text data. The acquisition, generation, storage and processing of multimedia data in computers and transmission over networks have grown tremendously in the recent past. In video surveillance, motion detection refers to the capability of the surveillance system to detect motion, identify the activity and capture the events while preventing the negligible activities. Motion detection is usually a software-based monitoring algorithm which, when it detects motions will signal the surveillance camera to begin capturing the event. Also called activity detection. An advanced motion detection surveillance system can analyze the type of motion to see if it warrants an alarm. In this research, we propose a novel motion detection algorithm for digital video recording (DVR) system using a Fuzzy Rule Based System that detects the motion in the running video intelligently.

As the size of multimedia database grows, it becomes impractical to manually annotate all contents and attributes of the media, and the difficulty in finding desired information increases. To copy with these challenges, content-based multimedia retrieval systems have been developed for various applications. The chapter not only provides a conceptual architecture for the design of content-based retrieval system, but also discusses essential components of retrieval system and their research issues, including feature extraction and representation, dimension reduction of feature vector, indexing, and query specifications. As content-based multimedia retrieval is a young research field and there exists many challenging research problems, this chapter also addresses several research issues for the future research.

Conventional database systems are designed for managing textual and numerical data, and retrieving such data is often based on simple comparisons of text/numerical values. However, this simple method of retrieval is no longer adequate for multimedia data, since the digitized representation of images, video, or data itself does not convey the reality of these media items. In addition, composite data consisting of heterogeneous types of data also associates with the semantic content acquired by a user's recognition. Therefore, content-based retrieval for multimedia data is realized taking such intrinsic features of multimedia data into account. Implementation of the content-based retrieval facility is not based on a single fundamental, but is closely related to an underlying data model, a priori knowledge of the area of interest, and the scheme for representing queries. This paper surveys recent studies on content-based retrieval for multimedia databases from the point of view of three fundamental issues. Throughout the discussion, we assume databases that manage only nontextual/numerical data, such as image or video, are also in the category of multimedia databases.

Similarity search is a key operation in several multimedia applications, including online Content-Based Multimedia Retrieval (CBMR) services. These applications have to deal with very large databases and are submitted to high query rates. In this context, scalability in distributed memory system is critical to assemble the required computing power and memory space. However, we have identified that the Data Equal Split (DES) parallelization and associated data partition strategy employed by the related works on the domain have limitations in terms of efficiency and scalability. Therefore, in this paper, we developed and implemented a framework for similarity search execution on distributed memory machines and proposed a novel class of data partition strategies that takes into account the data spatial organization in its distribution. This approach leads to a reduction in communication traffic and in costs associated with processing each task in local searches carried out in the distributed machine. Our approach attained a speedup of 2.4× on top of DES in the baseline case (5 nodes) and also achieves higher scalability efficiency and is 14.5× faster when 160 nodes are used. In fact, our novel data organization led to superlinear scalability in all configurations evaluated.