Face Animation

This chapter will discuss the multimedia modeling and specification methods, especially in the context of face animation. Personalized Face Animation is and/or can be a major user interface component in modern multimedia systems. After reviewing the related works in this area, we present the ShowFace streaming structure. This structure is based on most widely accepted industry standards in multimedia presentations like MPEG-4 and SMIL and extends them by providing a higher level Face Modeling Language (FML) for modeling and control purposes and by defining image transformations required for certain facial movements. ShowFace establishes a comprehensive framework for face animation consisting of components for parsing the input script, generating and splitting the audio and video “behaviors,” creating the required images and sounds, and eventually displaying or writing the data to files. This component-based design and scripted behavior make the framework suitable for many purposes including web-based applications.

Algorithm for the Retrieval of Image with Emergence Index in Multimedia

We discuss here emergence phenomenon where we study the hidden meanings of an image. We present this concept in image database access and retrieval of images using this as an index for retrieval. This would give an entirely different search outcome than an ordinary search where emergence is not considered, as consideration of hidden meanings could change the index of a search. We talk about emergence, emergence index and accessing multimedia databases using emergence index to locate geographic areas in this paper along with the algorithm.

Face Recognition Technology

Face recognition technology is one of the most widely used problems in computer vision. It is widely used in applications related to security and human-computer interfaces. The two reasons for this are the wide range of commercial and law enforcement applications and the availability of feasible technologies. In this chapter the various biometric systems and the commonly used techniques of face recognition, Feature Based, eigenface based, Line Based Approach and Local Feature Analysis are explained along with the results. A performance comparison of these algorithms is also given.

Technology of Music Score Watermarking

Content protection for multimedia data is widely recognized especially for data types that are frequently distributed, sold or shared in digital and via Internet. Particularly, the music industry dealing with audio files realized the necessity for content protection. Distribution of music sheets will face the same problems. Digital watermarking techniques provide a certain level of protection for music sheets. Classical image-oriented watermarking algorithms for images suffer from several drawbacks when directly applied to image representations of music sheets. Therefore, new solutions have been developed which are designed regarding the content of the music sheets. In comparison to other media types, the development of watermarking algorithms for music scores is a rather young technology. The chapter reviews the evolution of the early approaches and describes the current state of the art in the field.

A Duplicate Chinese Document Image Retrieval System Based on Line Segment Feature in Character Image Block

With the rapid progress of digital image technology, the management of duplicate document images is also emphasized widely. As a result, this paper suggests a duplicate Chinese document image retrieval (DCDIR) system, which uses the ratio of the number of black pixels to that of white pixels on the scanned line segments in a character image block as the feature of the character image block. Experimental results indicate that the system can indeed effectively and quickly retrieve the desired duplicate Chinese document image from a database.

Advances in Digital Home Photo Albums

In this chapter, we study the needs of digital home photo albums and the different components that are required to provide effective and efficient computer-based tools to match the users’ expectations for such systems. We focus mainly on indexing and retrieval of photographic images using symbolic descriptions of image contents. We describe how symbolic labeling of image regions can be achieved, how the representation of image content is achieved using two different kinds of representations supporting different needs, and how the retrieval is performed on these image descriptions. Other features of digital home photo management are also described, and our proposal is evaluated on a genuine collection of 2,400 home photographs.

Mind the Gap

The user interface is the principal component responsible for facilitating human-computer interaction in an information retrieval system and provides the medium between the end user and the system. Query formulation is a core activity in the process of information retrieval and a number of query paradigms have been proposed for specifying a query Qn in an image database I. Despite being identified as one of the major research areas, few studies have investigated the user interface for specifying visual queries and the area remains an open research issue. This chapter presents an overview of user interface research activity and related areas in the field of content-based image retrieval.

A Stochastic and Content-Based Image Retrieval Mechanism

Multimedia information, typically image information, is growing rapidly across the Internet and elsewhere. To keep pace with the increasing volumes of image information, new techniques need to be investigated to retrieve images intelligently and efficiently. Content-based image retrieval (CBIR) is always a challenging task. In this chapter, a stochastic mechanism, called Markov Model Mediator (MMM), is used to facilitate the searching and retrieval process for content-based image retrieval, which serves as the retrieval engine of the CBIR systems and uses stochastic-based similarity measures. Different from the common methods, our stochastic mechanism carries out the searching and similarity computing process dynamically, taking into consideration not only the image content features but also other characteristics of images such as their access frequencies and access patterns. Our experimental results demonstrate that the MMM mechanism together with the stochastic process can assist in retrieving more accurate results for user queries.

A Spatial Relationship Method Supports Image Indexing and Similarity Retrieval

The increasing availability of image and multimedia-oriented applications markedly impacts image/multimedia file and database systems. Image data are not well-defined keywords such as traditional text data used in searching and retrieving functions. Consequently, various indexing and retrieving methodologies must be defined based on the characteristics of image data. Spatial relationships represent an important feature of objects (called icons) in an image (or picture). Spatial representation by 2-D String and its variants, in a pictorial spatial database, has been attracting growing interest. However, most 2-D Strings represent spatial information by cutting the icons out of an image and associating them with many spatial operators. The similarity retrievals by 2-D Strings require massive geometric computation and focus only on those database images that have all the icons and spatial relationships of the query image. This study proposes a new spatial-relationship representation model called “Two Dimension Begin-End boundary string” (2D Be-string). The 2D Be-string represents an icon by its MBR boundaries. By applying “dummy objects,” the 2D Be-string can intuitively and naturally represent the pictorial spatial information without any spatial operator. A method of evaluating image similarities, based on the modified “Longest Common Subsequence” (LCS) algorithm, is presented. The proposed evaluation method can not only sift out those images of which all icons and their spatial relationships fully accord with query images, but for those images whose icons and/or spatial relationships are similar to those of query images. Problems of uncertainty in the query targets and/or spatial relationships thus solved. The representation model and similarity evaluation also simplify the retrieval progress of linear transformations, including rotation and reflection, of images.

Object-Based Video Analysis and Interpretation

In this chapter, we present a novel scheme for object-based video analysis and interpretation based on automatic video object extraction, video object abstraction, and semantic event modeling. In this scheme, video objects (VOs) are first automatically extracted, followed by a video object abstraction algorithm for identifying key frames to reduce data redundancy and provide reliable feature data for the next stage of the algorithm. Semantic feature modeling is based on a temporal variation of low-level features of video objects. Dynamic Bayesian networks (DBNs) are then used to characterize the spatio-temporal nature of the video objects. The system states in the proposed DBNs directly correspond to the physical concepts. Thus, the decoding of the DBN system states from observable variables is a natural interpretation of the behavior of the video objects. Since the video objects are generally considered as the dominant semantic features of video clips, the proposed scheme provides a powerful methodology for content description, which is critical for large scale MPEG-7 applications.