We have developed a Tv-on-demand system that provides playback of a television program after a period ranging from a few seconds to one week after broadcast, and have conducted usage trials in cooperation with a telev...
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We have developed a Tv-on-demand system that provides playback of a television program after a period ranging from a few seconds to one week after broadcast, and have conducted usage trials in cooperation with a television station in Nagano Prefecture of Japan. This system has been achieved through the development of various technologies such as automatic updating of stored television programs and contents retrieval by telop characters. Users in the trials can begin playback of a television program immediately after its broadcast has begun. The purpose of the trials was to evaluate the system's usability in applications such as contents retrieval, selective viewing of commercials, and customer service at the television station. This paper presents applied technologies and some experimental results and also addresses a new direction of information retrieval system based on the evaluation of the usage trials.
Content Based imageretrieval has recently become one of the most active research areas, due to the massive increase in the amount and complexity of digitized data being stored, transmitted and accessed. We present he...
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Content Based imageretrieval has recently become one of the most active research areas, due to the massive increase in the amount and complexity of digitized data being stored, transmitted and accessed. We present here a prototype implementation of DRAWSEARCH, an imageretrieval by content system that uses color and shape (and texture in the near future) features to index and retrieve images. The system, currently being tested and improved, is designed to increase interactivity with users posing queries over the Internet and avails of a Java client for query by sketch. It also implements relevance feedback to allow users dynamically refine queries. Experiments show that the proposed approach can greatly reduce the user's effort to compose a query while capturing his/her information need with greater precision.
This paper describes an API for image searching. The attempt was to isolate the functionality of the GUI from the functionality of the image search engine. The GUI would then make calls to the image search API and cou...
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This paper describes an API for image searching. The attempt was to isolate the functionality of the GUI from the functionality of the image search engine. The GUI would then make calls to the image search API and could be used with any image search engine implementing that API. Also, different methods of specifying the initial search image are discussed as well as different methods of displaying the results, including the use of 3D using vRML.
Queries referring to content embedded within images are an essential component of content-based search, browse, or summarize operations in imagedatabases. Localization of such queries under changes in appearance, occ...
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Queries referring to content embedded within images are an essential component of content-based search, browse, or summarize operations in imagedatabases. Localization of such queries under changes in appearance, occlusions and background clutter, is a difficult problem, for which current spatial access structures in databases are not suitable. In this paper we present a new method of indexing imagedatabases called location hashing that uses a special data structure called the location hash tree (LHT) for organizing feature information from images of a database. Location hashing is based on the principle of geometric hashing and determines simultaneously, the relevant images in the database and the regions within them that are most likely to contain a 2d pattern query without incurring detailed search of either. The location hash tree being a red-black tree, allows for efficient search for candidate locations using pose-invariant feature information derived from the query.
While current approaches for video segmentation and indexing are mostly focused on visual information, audio signals may actually play a primary role in video content parsing. In this paper, we present an approach for...
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While current approaches for video segmentation and indexing are mostly focused on visual information, audio signals may actually play a primary role in video content parsing. In this paper, we present an approach for automatic segmentation, indexing, and retrieval of audiovisual data based on audio content analysis. The accompanying audio signal of audiovisual data is first segmented and classified into basic types, i.e. speech, music, environmental sound, and silence. This coarse-level segmentation and indexing step is based on morphological and statistical analysis of several short-term features of the audio signals. Then, environmental sounds are classified into finer classes such as applause, explosion, bird's sound, etc. This fine-level classification and indexing step is based on time-frequency analysis of audio signals and the use of hidden Markov model (HMM) as the classifier. On top of this archiving scheme, an audiovisual data retrieval system is proposed. Experimental results show that the proposed approach has an accuracy rate higher than 90% for the coarse-level classification, and higher than 85% for the fine-level classification. Examples of audiovisual data segmentation and retrieval are also provided.
Recent research on imagedatabases has been aimed at the development of content-based retrieval techniques for the management of visual information. Compared with such visual information as color, texture, and spatial...
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Recent research on imagedatabases has been aimed at the development of content-based retrieval techniques for the management of visual information. Compared with such visual information as color, texture, and spatial constraints, shape is so important a feature associated with those image objects of interest that shape alone may be sufficient to identify and classify an object completely and accurately. This paper presents a novel method based on feature point histogram indexing for object shape representation in imagedatabases. In this scheme, the feature point histogram is obtained by discretizing the angles produced by the Delaunay triangulation of a set of unique feature points which characterize object shape in the context, and then counting the number of times each discrete angle occurs in the resulted triangulation. The proposed shape representation technique is translation, scale, and rotation independent. Our various experiments concluded that the Euclidean distance performs very well as the similarity measure function in combination with the feature point histogram computed by counting the two largest angles of each individual Delaunay triangle. Through the further experiment, we also found evidence that an image object representation using a feature point histogram provides an effective cue for image object discrimination.
This paper proposes an imageretrieval system which searches a database for images similar to a target imagined by a user. The system uses image features, rather than keywords, and retrieves images by reducing a multi...
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This paper proposes an imageretrieval system which searches a database for images similar to a target imagined by a user. The system uses image features, rather than keywords, and retrieves images by reducing a multidimensional feature space generated by the image feature vectors. First, the system presents the user some sample images with a suitable feature vector value and requires the user's interaction to obtain information on which image is similar to the target he/she has in his/her mind. Then, the information is used to appropriately reduce the feature space. This process is continued until the target region is reduced to a suitable volume. Since this method requires neither real target image nor keywords in retrieving, it is quite simple and practical. Experimental results show the advantage and efficiency of the proposed system.
Multimedia Information Systems are experiencing a tremendous growth as a direct consequence of the popularity and pervasive use of world wide web. As a consequence, it is becoming increasingly important to provide eff...
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Multimedia Information Systems are experiencing a tremendous growth as a direct consequence of the popularity and pervasive use of world wide web. As a consequence, it is becoming increasingly important to provide efficient and flexible solutions for accessing and retrieving multimedia data. images and video are emerging as significant data types in multimedia systems. And yet, most commercial systems are still text and key-word based and do not fully exploit the image content of these systems. We believe that there is an opportunity to build a novel interactive multimedia system for some specific applications in electronic commerce. In this paper we present an overview of our approach, the rationale behind it and the problems that are inherent in building such a system. We address some of the technical issues in representing and analysing image primitive features. These are the building blocks of any such systems. They can be generalized into a much broader range of applications as well.
This paper presents algorithms to deal with problems associated with indexing high-dimensional feature vectors that characterize video data. Indexing high dimensional vectors is well known to be computationally expens...
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This paper presents algorithms to deal with problems associated with indexing high-dimensional feature vectors that characterize video data. Indexing high dimensional vectors is well known to be computationally expensive. Our solution is to optimally split the high dimensional vector into a few low dimensional feature vectors and querying the system for each feature vector. This involves solving an important sub-problem: developing a model of retrieval that enables us to query the system efficiently. Once we formulate the retrieval problem in terms of a retrieval model, we present an optimality criterion to maximize the number of results using this model. The criterion is based on a novel idea of using the underlying probability distribution of the feature vectors. A branch-and-prune strategy optimized per each query, is developed. This uses the set of features derived from the optimality criterion. Our results show that the algorithm performs well, giving a speedup of a factor of 25 with respect to a linear search while retaining the same level of Recall.
In this paper we address the problem of choosing appropriate features to describe the content of still pictures or video sequences including audio. As the computational analysis of these features is often time-consumi...
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In this paper we address the problem of choosing appropriate features to describe the content of still pictures or video sequences including audio. As the computational analysis of these features is often time-consuming it is useful to identify a minimal set allowing for an automatic classification of some class or genre. Further it can be shown that deleting the coherence of the features characterizing some class is not suitable to guarantee an optimal classification result. The central question of the paper is thus which features should be selected and how they should be weighted to optimize a classification problem.
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