EDUPMO

Engineering is seen today as a synonym of innovation, especially for providing technological solutions that affect not only daily work and entertainment, but education as well. Project portfolio management of multimedia production and use emerges today as a challenge both for the enrichment of traditional classroom based teaching and for distance education offering. In this way, this chapter intends to answer the following question: Which are the fundamental aspects to be considered in the management of projects on educational multimedia production and use? This research presents a proposal of a project management model for digital content production and use. The model, the methodology and the implementation will be named EduPMO, an abbreviation of Educational Project Management Office. Therefore, the model, the methodology, and the implementation should be understood as related but independent entities. This interdisciplinary investigation involves different topics, going from metadata and interoperability to intellectual property and process improvement.

Abstractions in Intelligent Multimedia Databases

Semantic characterization is necessary for developing intelligent multimedia databases, because humans tend to search for media content based on their inherent semantics. However, automated inference of semantic concepts derived from media components stored in a database is still a challenge. The aim of this chapter is to demonstrate how layered architectures and “visual keywords” can be used to develop intelligent search systems for multimedia databases. The layered architecture is used to extract meta-data from multimedia components at various layers of abstractions. While the lower layers handle physical file attributes and low-level features, the upper layers handle high-level features and attempts to remove ambiguities inherent in them. To access the various abstracted features, a query schema is presented, which provides a single point of access while establishing hierarchical pathways between feature-classes. Minimization of the semantic gap is addressed using the concept of “visual keyword” (VK). “Visual keywords” are segmented portions of images with associated low- and high-level features, implemented within a semantic layer on top of the standard low-level features layer, for characterizing semantic content in media components. Semantic information is however predominantly expressed in textual form, and hence is susceptible to the limitations of textual descriptors – viz. ambiguities related to synonyms, homonyms, hypernyms, and hyponyms. To handle such ambiguities, this chapter proposes a domain specific ontology-based layer on top of the semantic layer, to increase the effectiveness of the search process.

Towards a Dynamic Semantic and Complex Relationship Modeling of Multimedia Data

Multimedia data is a challenge for data management. The semantics of traditional alphanumeric data are mostly explicit, unique, and self-contained, but the semantics of multimedia data are usually dynamic, diversiform, and varying from one user’s perspective to another’s. When dealing with different applications in which multimedia data is involved, great challenges arise. We first introduce a novel data model called Information Networking Model (INM), which can represent the dynamic and complex semantic relationships of the real world. In this chapter, we show how to use INM to capture dynamic and complex semantics relationship of multimedia data. Using INM, we present a multimedia modeling mechanism. The general idea of this novel mechanism is to place the multimedia data in a complex semantic environment based on the real world or application requirements, and then users can make use of both contextual semantics and multimedia metadata to retrieve the precise results they expect.

Latent Semantic Analysis for Text Mining and Beyond

Latent Semantic Analysis (LSA) or Latent Semantic Indexing (LSI), when applied to information retrieval, has been a major analysis approach in text mining. It is an extension of the vector space method in information retrieval, representing documents as numerical vectors but using a more sophisticated mathematical approach to characterize the essential features of the documents and reduce the number of features in the search space. This chapter summarizes several major approaches to this dimensionality reduction, each of which has strengths and weaknesses, and it describes recent breakthroughs and advances. It shows how the constructs and products of LSA applications can be made user-interpretable and reviews applications of LSA beyond information retrieval, in particular, to text information visualization. While the major application of LSA is for text mining, it is also highly applicable to cross-language information retrieval, Web mining, and analysis of text transcribed from speech and textual information in video.

Normal Forms for Multimedia Databases

In this chapter we present normal forms for the design of multimedia database schemes with reduced manipulation anomalies. To this aim we first discuss how to describe the semantics of multimedia attributes based upon the concept of generalized icons, already used in the modeling of multimedia languages. Then, we introduce new extended dependencies involving different types of multimedia data. Such dependencies are based on domain specific similarity measures that are used to detect semantic relationships between complex data types. Based upon these new dependencies, we have defined five normal forms for multimedia databases, some focusing on the level of segmentation of multimedia attributes, others on the level of fragmentation of tables.

A Hybrid Approach to Content-Based Image Retrieval

Digital image storage and retrieval is gaining more popularity due to the rapidly advancing technology and the large number of vital applications, in addition to flexibility in managing personal collections of images. Traditional approaches employ keyword based indexing which is not very effective. Content based methods are more attractive though challenging and require considerable effort for automated feature extraction. In this chapter, we present a hybrid method for extracting features from images using a combination of already established methods, allowing them to be compared to a given input image as seen in other query-by-example methods. First, the image features are calculated using Edge Orientation Autocorrelograms and Color Correlograms. Then, distances of the images to the original image will be calculated using the L1 distance feature separately for both features. The distance sets will then be merged according to a weight supplied by the user. The reported test results demonstrate the applicability and effectiveness of the proposed approach.

Approach of Using Texture and Shape for Image Retrieval

Feature integration is one of important research contents in content-based image retrieval. Single feature extraction and description is foundation of the feature integration. Features from a single feature extraction approach are a single feature or composite features, whether integration features are more discriminative than them or not. An approach of integrating shape and texture features was presented and used to study these problems. Gabor wavelet transform with minimum information redundancy was used to extract texture features, which would be used for feature analyses. Fourier descriptor approach with brightness was used to extract shape features. Then both features were integrated in parallel by weights. Comparisons were carried out among the integration features, the texture features, and the shape features, so that discrimination of the integration features can be testified.

Towards a Unified Multimedia Metadata Management Solution

With increasing use of multimedia in various domains, several metadata standards appeared these last decades in order to facilitate the manipulation of multimedia contents. These standards help consumers to search content they desire and to adapt the retrieved content according to consumers’ profiles and preferences. However, in order to extract information from a given standard, a user must have a pre-knowledge about this latest. This condition is not easy to satisfy due to the increasing number of available standards. In this chapter, we introduce some of the main de facto multimedia standards that cover the description, by means of metadata, of the content and of the use context (profiles, devices, networks…). We discuss then the benefits of proposing an integrated vision of multimedia metadata standards through the usage of a generic multimedia metadata integration system, and we expose the challenges of its implementation.

Profiling User Color Perception for Image Retrieving

The aim of this work is to build a user profile according to his own perception of colors for image retrieving. Images are being processed relying on a standard or initial set of parameters using the fuzzy set theory and the HLS color space (Hue, Lightness, and Saturation). We developed a dynamic construction of the user profile, which will increase his satisfaction by being more personalized and accommodated to his particular needs. We suggest two methods to define the perception and transform it into a profile; the first method is achieved by querying the user and getting answers, which will guide through the process of implementation of the profile; the second method is achieved by comparing different subjects and ending up by an appropriate aggregation. We also present a method that will recalculate the amount of colors in the image based on another set of parameters, so the colorimetric profile of the image is being modified accordingly. Avoiding the repetition of the process at the pixel level is the main target of this phase, because reprocessing each image is time consuming and turned to be not feasible.

Fuzzy Logic for Image Retrieval and Image Databases

Fuzzy set theory has been extensively applied to the representation and processing of imprecise and uncertain data. Image data is becoming an important data resource with rapid growth in the number of large-scale image repositories. However, image data is fuzzy in nature, and imprecision and vagueness may exist in both image descriptions and query specifications. This chapter reviews some major work of image retrieval with fuzzy logic in the literature, including fuzzy content-based image retrieval and database support for fuzzy image retrieval. For the fuzzy content-based image retrieval, we present how fuzzy sets are applied for the extraction and representation of visual (colors, shapes, textures) features, similarity measures and indexing, relevance feedback, and retrieval systems. For the fuzzy image database retrieval, we present how fuzzy sets are applied for fuzzy image query processing based on a defined database models, and how various fuzzy database models can support image data management.