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Capturing high-level image concepts via affinity relationships in image database retrieval
In this paper, we present a mechanism called Markov Model Mediator (MMM) to facilitate the efficient and effective capturing of high-level image concepts in content-based image retrieval (CBIR). MMM serves as the retrieval engine of the CBIR system and uses affinity-based similarity measures. This mechanism is effective in capturing subjective user concepts in that it not only takes into consideration the global image features, but also learns the high-level concepts of the images from the history of user access patterns and access frequencies on the images in the image database, which differentiates it from the common methods in CBIR. The advantage of our proposed mechanism is that it exploits the richness in the structured description of visual contents as well as the relative affinity relationships among the images. Consequently, it provides the capability to bridge the gap between the low-level features and the high-level concepts. This mechanism is also efficient in that it integrates Principal Component Analysis (PCA) to significantly reduce the image search space at a low cost before performing exact similarity matching. An off-line training subsystem for this framework was implemented and integrated into our system. The experimental results demonstrate that MMM can effectively capture user’s high-level concept more quickly
Knowledge assisted data management and retrieval in multimedia database sistems
With the proliferation of multimedia data and ever-growing requests for multimedia applications, there is an increasing need for efficient and effective indexing, storage and retrieval of multimedia data, such as graphics, images, animation, video, audio and text. Due to the special characteristics of the multimedia data, the Multimedia Database management Systems (MMDBMSs) have emerged and attracted great research attention in recent years. Though much research effort has been devoted to this area, it is still far from maturity and there exist many open issues. In this dissertation, with the focus of addressing three of the essential challenges in developing the MMDBMS, namely, semantic gap, perception subjectivity and data organization, a systematic and integrated framework is proposed with video database and image database serving as the testbed. In particular, the framework addresses these challenges separately yet coherently from three main aspects of a MMDBMS: multimedia data representation, indexing and retrieval. In terms of multimedia data representation, the key to address the semantic gap issue is to intelligently and automatically model the mid-level representation and/or semi-semantic descriptors besides the extraction of the low-level media features. The data organization challenge is mainly addressed by the aspect of media indexing where various levels of indexing are required to support the diverse query requirements. In particular, the focus of this study is to facilitate the high-level video indexing by proposing a multimodal event mining framework associated with temporal knowledge discovery approaches. With respect to the perception subjectivity issue, advanced techniques are proposed to support users’ interaction and to effectively model users’ perception from the feedback at both the image-level and object-level