A Radial Basis Function and Semantic Learning Space Based Composite Learning Approach to Image Retrieval

This paper introduces a composite learning approach for image retrieval with relevance feedback. The proposed system combines the radial basis function (RBF) based low-level learning and the semantic learning space (SLS) based high-level learning to retrieve the desired images with fewer than 3 feedback steps. User’s relevance feedback is utilized for updating both low-level and high-level features of the query image. Specifically, the RBF-based learning captures the non-linear relationship between the low-level features and the semantic meaning of an image. The SLS-based learning stores semantic features of each database image using randomly chosen semantic basis images. The similarity score is computed as the weighted combination of normalized similarity scores yielded from both RBF and SLS learning. Extensive experiments evaluate the performance of the proposed approach and demonstrate our system achieves higher retrieval accuracy than peer systems. Index Terms — Radial basis function, semanti

Automated Semantic Content Extraction from Images

In this study, an automatic semantic segmentation and object recognition methodology is implemented which bridges the semantic gap between low level features of image content and high level conceptual meaning. Semantically understanding an image is essential in modeling autonomous robots, targeting customers in marketing or reverse engineering of building information modeling in the construction industry. To achieve an understanding of a room from a single image we proposed a new object recognition framework which has four major components: segmentation, scene detection, conceptual cueing and object recognition. The new segmentation methodology developed in this research extends Felzenswalb\u27s cost function to include new surface index and depth features as well as color, texture and normal features to overcome issues of occlusion and shadowing commonly found in images. Adding depth allows capturing new features for object recognition stage to achieve high accuracy compared to the current state of the art. The goal was to develop an approach to capture and label perceptually important regions which often reflect global representation and understanding of the image. We developed a system by using contextual and common sense information for improving object recognition and scene detection, and fused the information from scene and objects to reduce the level of uncertainty. This study in addition to improving segmentation, scene detection and object recognition, can be used in applications that require physical parsing of the image into objects, surfaces and their relations. The applications include robotics, social networking, intelligence and anti-terrorism efforts, criminal investigations and security, marketing, and building information modeling in the construction industry. In this dissertation a structural framework (ontology) is developed that generates text descriptions based on understanding of objects, structures and the attributes of an image

Semantic image retrieval using relevance feedback and transaction logs

Due to the recent improvements in digital photography and storage capacity, storing large amounts of images has been made possible, and efficient means to retrieve images matching a user’s query are needed. Content-based Image Retrieval (CBIR) systems automatically extract image contents based on image features, i.e. color, texture, and shape. Relevance feedback methods are applied to CBIR to integrate users’ perceptions and reduce the gap between high-level image semantics and low-level image features. The precision of a CBIR system in retrieving semantically rich (complex) images is improved in this dissertation work by making advancements in three areas of a CBIR system: input, process, and output. The input of the system includes a mechanism that provides the user with required tools to build and modify her query through feedbacks. Users behavioral in CBIR environments are studied, and a new feedback methodology is presented to efficiently capture users’ image perceptions. The process element includes image learning and retrieval algorithms. A Long-term image retrieval algorithm (LTL), which learns image semantics from prior search results available in the system’s transaction history, is developed using Factor Analysis. Another algorithm, a short-term learner (STL) that captures user’s image perceptions based on image features and user’s feedbacks in the on-going transaction, is developed based on Linear Discriminant Analysis. Then, a mechanism is introduced to integrate these two algorithms to one retrieval procedure. Finally, a retrieval strategy that includes learning and searching phases is defined for arranging images in the output of the system. The developed relevance feedback methodology proved to reduce the effect of human subjectivity in providing feedbacks for complex images. Retrieval algorithms were applied to images with different degrees of complexity. LTL is efficient in extracting the semantics of complex images that have a history in the system. STL is suitable for query and images that can be effectively represented by their image features. Therefore, the performance of the system in retrieving images with visual and conceptual complexities was improved when both algorithms were applied simultaneously. Finally, the strategy of retrieval phases demonstrated promising results when the query complexity increases

Extensibility in ORDBMS databases : an exploration of the data cartridge mechanism in Oracle9i

To support current and emerging database applications, Object-Relational Database Management Systems (ORDBMS) provide mechanisms to extend the data storage capabilities and the functionality of the database with application-specific types and methods. Using these mechanisms, the database may contain user-defined data types, large objects (LOBs), external procedures, extensible indexing, query optimisation techniques and other features that are treated in the same way as built-in database features . The many extensibility options provided by the ORDBMS, however, raise several implementation challenges that are not always obvious. This thesis examines a few of the key challenges that arise when extending Oracle database with new functionality. To realise the potential of extensibility in Oracle, the thesis used the problem area of image retrieval as the main test domain. Current research efforts in image retrieval are lagging behind the required retrieval, but are continuously improving. As better retrieval techniques become available, it is important that they are integrated into the available database systems to facilitate improved retrieval. The thesis also reports on the practical experiences gained from integrating an extensible indexing scenario. Sample scenarios are integrated in Oracle9i database using the data cartridge mechanism, which allows Oracle database functionality to be extended with new functional components. The integration demonstrates how additional functionality may be effectively applied to both general and specialised domains in the database. It also reveals alternative design options that allow data cartridge developers, most of who are not database server experts, to extend the database. The thesis is concluded with some of the key observations and options that designers must consider when extending the database with new functionality. The main challenges for developers are the learning curve required to understand the data cartridge framework and the ability to adapt already developed code within the constraints of the data cartridge using the provided extensibility APls. Maximum reusability relies on making good choices for the basic functions, out of which specialised functions can be built.KMBT_363Adobe Acrobat 9.54 Paper Capture Plug-i