The proceedings contain 47 papers from the conference on proceedings of: storage and retrieval for mediadatabases2003. The topics discussed include: visual interfaces for a semantic content-based image retrieval sys...
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The proceedings contain 47 papers from the conference on proceedings of: storage and retrieval for mediadatabases2003. The topics discussed include: visual interfaces for a semantic content-based image retrieval system;managing and searching personal photocollections;selecting image retrieval parameters with a genetic algorithms;image object search combining color with Gabor wavelet shape descriptors;the role of classifiers in multimedia content management;and supervised multimedia categorization.
In an earlier study a Semantic Content Based Image retrieval system was developed. The system requires a Visual Object Process Diagram - VOPD to be created for each image in the database and for the query. This is a m...
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ISBN:
(纸本)0819448214
In an earlier study a Semantic Content Based Image retrieval system was developed. The system requires a Visual Object Process Diagram - VOPD to be created for each image in the database and for the query. This is a major drawback since it requires the user and database manager to be acquainted with the rules and structures of the VOPD. This is not trivial and in fact troublesome to the naive user. To overcome this drawback two approaches are presented in this work, to provide an interface to the Image retrieval system and to bypass the need of manually creating VOPD representations.
Image retrieval (IR) means taking a probe image and finding the most appropriate match in a (possibly very large) image database. Unlike keyword-indexing, our approach is to compute a feature vector (FV) for each imag...
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ISBN:
(纸本)0819448214
Image retrieval (IR) means taking a probe image and finding the most appropriate match in a (possibly very large) image database. Unlike keyword-indexing, our approach is to compute a feature vector (FV) for each image, and to compute the distance from the probe to each image in the database. As a starting point, we studied the system of Jacobs et al., developed at the University of Washington, which used the Haar wavelet transform to produce feature vectors from images. A genetic algorithm developed weighting parameters which yielded significantly improved image retrieval performance.
Video contains multiple types of audio and visual information, which are difficult to extract, combine or trade-off in general video information retrieval. This paper provides an evaluation on the effects of different...
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ISBN:
(纸本)0819448214
Video contains multiple types of audio and visual information, which are difficult to extract, combine or trade-off in general video information retrieval. This paper provides an evaluation on the effects of different types of information used for video retrieval from a video collection. A number of different sources of information are present in most typical broadcast video collections and can be exploited for information retrieval. We will discuss the contributions of automatically recognized speech transcripts, image similarity matching, face detection and video OCR in the contexts of experiments performed as part of 2001 TREC Video retrieval Track evaluation performed by the National Institute of Standards and Technology. For the queries used in this evaluation, image matching and video OCR proved to be the deciding aspects of video information retrieval.
An original image retrieval framework is proposed and developed. Trying to achieve the semantic retrieval, a novel cognitive model - feature element constructional model is proposed. With its hierachical constructiona...
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ISBN:
(纸本)0819448214
An original image retrieval framework is proposed and developed. Trying to achieve the semantic retrieval, a novel cognitive model - feature element constructional model is proposed. With its hierachical constructional structure and bias competition mechanism, the new model provides great power for semantic retrieval. Two types of retrieval mode are presented in the new system, which both try to analysis the semantic concept in the query image or semantic command. Then matching from the object to the feature element is carried out to obtain the final result, and our understanding of retrieval "to provide the way of approaching the accurate result" is also embodied.
Relevance feedback in content-based image retrieval has been an active research focus for many years. It uses user-labeled information to re-adjust the measurement of similarity between images to get the improved retr...
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ISBN:
(纸本)0819448214
Relevance feedback in content-based image retrieval has been an active research focus for many years. It uses user-labeled information to re-adjust the measurement of similarity between images to get the improved retrieval results. In this paper we propose a simple and effective approach for image relevance feedback, which uses both positive and negative examples labeled by users to refine the query and update the distance measurement dynamically. Our method not only has a very low complexity but also adapts well to the changes of user's retrieval interests. Experimental results on a database of 7,000 images represented by MPEG-7 color and texture descriptors show the efficiency of our algorithm compared with other two existing algorithms.
We propose semantic event detection method using MPEG-7. In the proposed method, content description technique of MPEG-7 is adopted into the detection algorithm to extract, represent, reuse, and interoperate low-level...
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ISBN:
(纸本)0819448214
We propose semantic event detection method using MPEG-7. In the proposed method, content description technique of MPEG-7 is adopted into the detection algorithm to extract, represent, reuse, and interoperate low-level features. Also we use multiple descriptors to improve efficiency. In this paper, shots and key frames give a hint in semantic event detection by predefined inference. Each shot gets a semantic meaning using MPEG-7 descriptors with example image or image sequence. Event is detected by segmenting the shots.
Enabling semantic detection and indexing is an important task in multimedia content management. Learning and classification techniques are increasingly relevant to the state of the art content management systems. From...
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ISBN:
(纸本)0819448214
Enabling semantic detection and indexing is an important task in multimedia content management. Learning and classification techniques are increasingly relevant to the state of the art content management systems. From relevance feedback to semantic detection, there is a shift in the amount of supervision that precedes retrieval from light weight classifiers to heavy weight classifiers. In this paper we compare the performance of some popular classifiers for semantic video indexing. We mainly compare among other techniques, one technique for generative modeling and one for discriminant learning and show how they behave depending on the number of examples that the user is willing to provide to the system. We report results using the NIST TREC Video Corpus.
We present a framework for analyzing the structure of digital media streams. Though our methods work for video, text, and audio, we concentrate on detecting the structure of digital music files. In the first step, spe...
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ISBN:
(纸本)0819448214
We present a framework for analyzing the structure of digital media streams. Though our methods work for video, text, and audio, we concentrate on detecting the structure of digital music files. In the first step, spectral data is used to construct a similarity matrix calculated from inter-frame spectral similarity. The digital audio can be robustly segmented by correlating a kernel along the diagonal of the similarity matrix. Once segmented, spectral statistics of each segment are computed. In the second step, segments are clustered based on the self-similarity of their statistics. This reveals the structure of the digital music in a set of segment boundaries and labels. Finally, the music can be summarized by selecting clusters with repeated segments throughout the piece. The summaries can be customized for various applications based on the structure of the original music.
Content-based image retrieval has become an active research topic for more than one decade. Nevertheless, current image retrieval systems still have major difficulties bridging the gap between the user's implied c...
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ISBN:
(纸本)0819448214
Content-based image retrieval has become an active research topic for more than one decade. Nevertheless, current image retrieval systems still have major difficulties bridging the gap between the user's implied concept and the low-level image description. To address the difficulties, this paper presents a novel image retrieval model integrating long-term learning with short-term learning. This model constructs a semantic image link network by long-term learning which simply accumulates previous users' relevance feedback. Then, the semantic information learned from long-term learning process guides short-term learning of a new user. The image retrieval is based on a seamless joint of both long-term learning and short-term learning. The model is easy to implement and can be efficiently applied to a practical image retrieval system. Experimental results on 10,000 images demonstrate that the proposed model is promising.
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