Thesis (Ph. D.)--University of Washington, 2001. / Vita. Includes bibliographical references (leaves 245-272).

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Digital multimedia content production and the amount of content present all over the world have exploded in the recent years. The consequences of this fact can be observed everywhere in many different forms, to exemplify, huge digital video archives of broadcasting companies, commercial image archives, virtual museums, etc. In order for these sources to be useful and accessible, this technological advance must be accompanied by the effective techniques of indexing and retrieval. The most effective way of indexing is the one providing a basis for retrieval in terms of semantic concepts, upon which ordinary users of multimedia databases base their queries. On the other hand, semantic classification of images using low-level features is a challenging problem. Combining experts with different classifier structures, trained by MPEG-7low-level color and texture descriptors, is examined as a solution alternative. For combining different classifiers and features, advanced decision mechanisms are proposed, which utilize basic expert combination strategies in different settings. Each of these decision mechanisms, namely Single Feature Combination (SFC), Multiple Feature Direct Combination (MFDC), and Multiple Feature Cascaded Combination (MFCC) enjoy significant classification performance improvements over single experts. Simulations are conducted on eight different visual semantic classes, resulting in accuracy improvements between 3.5-6.5%, when they are compared with the best performance of single expert systems.

The focus of this thesis is the definition of a complete framework for texture-based annotation and retrieval. This framework is centred on the concept of "texture codes", so called because they encode the relative energy levels of Gabor filter responses. These codes are pixel-based, robust descriptors with respect to illumination variations, can be generated efficiently, and included in a fast retrieval process. They can act as local or global descriptors, and can be used in the representations of regions or objects. Our framework is therefore capable of supporting a wide range of queries and applications. During our research, we have been able to utilise results of psychological studies on the perception of similarity and have explored non-metric similarity scores. As a result, we have found that similarity can be evaluated with simple measures predominantly relying on the information extracted from the query, without a drastic loss in retrieval performance. We have been able to show that the most simple measure possible, counting the number of common codes between the query and a stored image, can for some algorithmic parameters outperform well-proven benchmarks. Importantly also, our measures can all support partial comparisons, so that region-based queries can be answered without the need for segmentation. We have investigated refinements of the framework which endow it with the ability to localise queries in candidate images, and to deal with user relevance feedback. The final framework can generate good and fast retrieval results as demonstrated with a databases of 3723 images, and can therefore be useful as a stand-alone system. The framework has also been applied to the problem of high-level annotation. In particular, it has been used as a cue detector, where a cue is a visual example of a particular concept such as a type of sport. The detection results show that the system can predict the correct cue among a small set of cues, and can therefore provide useful information to an engine fusing the outputs of several cue detectors. So an important aspect of this framework is that it is expected to be an asset within a multi-cue annotation and/or retrieval system.

English language education in Brazilian private school systems is undergoing changes. Several school systems have opted for the implementation of an American high school curriculum using a Content-based Instruction (CBI) or Content and Language Integrated Learning (CLIL) curricular framework within which students apply their language skills to learning subject specific academic content. High School International (HSI) is one of the providers of a CLIL curriculum. In this descriptive case study of the implementation of the HSI CLIL curriculum in a private boarding school in the Southeast of Brazil respective stakeholders were interviewed and their experiences have been described and analyzed. Certain critical aspects have been identified. Involvement of the school principal and administration is one of the key ingredients of a successful implementation. Planning the implementation months in advance, such as preparing the curriculum, course outlines, and schedules, training of teachers, staff, and academic coordinator, definitely eases the whole process.

Content-Based Image Retrieval has been an active research area for decades. In a CBIR system, one or more images are used as query to search for similar images. The similarity is measured on the low level features, such as color, shape, edge, texture. First, each image is processed and visual features are extract. Therefore each image becomes a point in the feature space. Then, if two images are close to each other in the feature space, they are considered similar. That is, the k nearest neighbors are considered the most similar images to the query image. In this K-Nearest Neighbor (k-NN) model, semantically similar images are assumed to be clustered together in a single neighborhood in the high-dimensional feature space. Unfortunately semantically similar images with different appearances are often clustered into distinct neighborhoods, which might scatter in the feature space. Hence, confinement of the search results to a single neighborhood is the latent reason of the low recall rate of typical nearest neighbor techniques. In this dissertation, a new image retrieval technique - the Query Decomposition (QD) model is introduced. QD facilitates retrieval of semantically similar images from multiple neighborhoods in the feature space and hence bridges the semantic gap between the images’ low-level feature and the high-level semantic meaning. In the QD model, a query may be decomposed into multiple subqueries based on the user’s relevance feedback to cover multiple image clusters which contain semantically similar images. The retrieval results are the k most similar images from multiple discontinuous relevant clusters. To apply the benifit from QD study, a mobile client-side relevance feedback study was conducted. With the proliferation of handheld devices, the demand of multimedia information retrieval on mobile devices has attracted more attention. A relevance feedback information retrieval process usually includes several rounds of query refinement. Each round incurs exchange of tens of images between the mobile device and the server. With limited wireless bandwidth, this process can incur substantial delay making the system unfriendly iii to use. The Relevance Feedback Support (RFS) structure that was designed in QD technique was adopted for Client-side Relevance Feedback (CRF). Since relevance feedback is done on client side, system response is instantaneous significantly enhancing system usability. Furthermore, since the server is not involved in relevance feedback processing, it is able to support thousands more users simultaneously. As the QD technique improves on the accuracy of CBIR systems, another study, which is called In-Memory relevance feedback is studied in this dissertation. In the study, we improved the efficiency of the CBIR systems. Current methods rely on searching the database, stored on disks, in each round of relevance feedback. This strategy incurs long delay making relevance feedback less friendly to the user, especially for very large databases. Thus, scalability is a limitation of existing solutions. The proposed in-memory relevance feedback technique substantially reduce the delay associated with feedback processing, and therefore improve system usability. A data-independent dimensionality-reduction technique is used to compress the metadata to build a small in-memory database to support relevance feedback operations with minimal disk accesses. The performance of this approach is compared with conventional relevance feedback techniques in terms of computation efficiency and retrieval accuracy. The results indicate that the new technique substantially reduces response time for user feedback while maintaining the quality of the retrieval. In the previous studies, the QD technique relies on a pre-defined Relevance Support Support structure. As the result and user experience indicated that the structure might confine the search range and affect the result. In this dissertation, a novel Multiple Direction Search framework for semi-automatic annotation propagation is studied. In this system, the user interacts with the system to provide example images and the corresponding annotations during the annotation propagation process. In each iteration, the example images are dynamically clustered and the corresponding annotations are propagated separately to each cluster: images in the local neighborhood are annotated. Furthermore, some of those images are returned to the user for further annotation. As the user marks more images, iv the annotation process goes into multiple directions in the feature space. The query movements can be treated as multiple path navigation. Each path could be further split based on the user’s input. In this manner, the system provides accurate annotation assistance to the user - images with the same semantic meaning but different visual characteristics can be handled effectively. From comprehensive experiments on Corel and U. of Washington image databases, the proposed technique shows accuracy and efficiency on annotating image databases.

The field of biometrics has grown significantly in the past decade due to an increase in interest from law enforcement. Law enforcement officials are interested in adding tattoos alongside irises and fingerprints to their toolbox of biometrics. They often use these biometrics to aid in the identification of victims and suspects. Like facial recognition, tattoos have seen a spike in attention over the past few years. Tattoos, however, have not received as much attention by researchers. This lack of attention towards tattoos stems from the difficulty inherent in matching these tattoos. Such difficulties include image quality, affine transformation, warping of tattoos around the body, and in some cases, excessive body hair covering the tattoo. We will utilize context-based image retrieval to find a tattoo in a database which means using one image to query against a database in order to find similar tattoos. We will focus specifically on the keypoint detection process in computer vision. In addition, we are interested in finding not just exact matches but also similar tattoos. We will conclude that the ORB detector pulls the most relevant features and thus is the best chance for yielding an accurate result from content-based image retrieval for tattoos. However, we will also show that even ORB will not work on its own in a content-based image retrieval system. Other processes will have to be involved in order to return accurate matches. We will give recommendations on next-steps to create a better tattoo retrieval system.

Most existing Peer-to-Peer (P2P) systems support only title-based searches and are limited in functionality when compared to today’s search engines. In this paper, we present the design of a distributed P2P information sharing system that supports semantic-based content searches of relevant documents. First, we propose a general and extensible framework for searching similar documents in P2P network. The framework is based on the novel concept of Hierarchical Summary Structure. Second, based on the framework, we develop our efficient document searching system, by effectively summarizing and maintaining all documents within the network with different granularity. Finally, an experimental study is conducted on a real P2P prototype, and a large-scale network is further simulated. The results show the effectiveness, efficiency and scalability of the proposed system. / Singapore-MIT Alliance (SMA)

Ever since the rapid growth of the Internet, recommender systems have become essential in helping online users to search and retrieve relevant information they need. Just like the situation that people rely heavily on recommendation in their daily decision making processes, online users may identify desired documents more effectively and efficiently through recommendation of other users who exhibit similar interests, and/or through extracting crucial features of the users¡¦ past preferences. Typical recommendation approaches can be classified into collaborative filtering and content-based filtering. Both approaches, however, have their own drawbacks. The purpose of this research is thus to propose a hybrid approach for text recommendations. We combine collaborative input and document content to facilitate the creation of extended content-based user profiles. These profiles are then rearranged with the technique of latent semantic indexing. Two experiments are conducted to verify our proposed approach. The objective of these experiments is to compare the recommendation results from our proposed approach with those from the other two approaches. The results show that our approach is capable of distinguishing different degrees of document preference, and makes appropriate recommendation to users or does not make recommendation to users for uninterested documents. The application of our proposed approach is justified accordingly.

Over the last few years, peer-to-peer (P2P) applications have relentlessly grown to represent a formidable component of Internet traffic. In contract to P2P networks witch used well-defined port number, current P2P applications have use of arbitrary ports. As P2P applications continue to evolve, robust and effective methods are methods are needed for P2P traffic identification. Many P2P applications are bandwidth-intensive. Understanding the Internet traffic profile is important for several reasons, including traffic engineering, network service pricing. In this Thesis, we integrated port-based method into original Classifier which is using content-based method only. Therefore, we can improve the recognition rate for Classifier and identify more applications. We also verified our Classifier recognition rate by using the results of Service Control Engine.