video parsing is an important step in content-based indexing techniques where the input video is decomposed into segments with uniform content. In video parsing detection of scene changes is one of the approaches wide...
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video parsing is an important step in content-based indexing techniques where the input video is decomposed into segments with uniform content. In video parsing detection of scene changes is one of the approaches widely used for extracting key frames from the video sequence. In this paper, an algorithm based on motion vectors is proposed to detect sudden scene changes and gradual scene changes (camera movements such as panning, tilting and zooming). Unlike some of the existing schemes, the proposed scheme is capable of detecting both sudden and gradual changes in uncompressed as well as compressed domain video. It is shown that the resultant motion vector can be used to identify and classify gradual changes due to camera movements. Results show that algorithm performed as well as the histogram-based schemes with uncompressed video. The performance of the algorithm was also investigated with H.263 compressed video. The detection and classification of both sudden and gradual scene changes was successfully demonstrated.
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.
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.
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.
A simultaneous learning and indexing technique is proposed for efficient content-based retrieval of images that can be described by feature vectors. This technique builds a compact high-dimensional index while taking ...
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A simultaneous learning and indexing technique is proposed for efficient content-based retrieval of images that can be described by feature vectors. This technique builds a compact high-dimensional index while taking into account that the raw feature space needs to be adjusted for each new application. With this technique, much better efficiency can be achieved as compared to those techniques that do not make provisions for efficient indexing.
For the last few years, shot boundary detection has been recognized as an important research issue on videoretrieval. Also as a preliminary step for the task, it is essential to extract salient features from videos. ...
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For the last few years, shot boundary detection has been recognized as an important research issue on videoretrieval. Also as a preliminary step for the task, it is essential to extract salient features from videos. Recently, it has become common to perform the two tasks in compressed domain to alleviate their computational costs. In this paper, we propose novel shot boundary detection technique, which uses two feature images, or DC and edge images, extracted directly from MPEG compressed video. While a DC image can be easily obtained, edge image extraction usually requires considerable computational burden. For fast edge image extraction, we suggest to utilize only a few AC coefficients of each DCT block in motion compensated P-frames and B-frames as well as I-frames. This drastically reduces the computational burden compared to edge extraction in the spatial domain. In order to further reduce the computational burden, another edge image extraction technique is also suggested on the basis of AC prediction using DC images. By using the edge energy diagram obtained from edge images and histograms from DC images, shot boundaries such as abrupt transitions, fades, and dissolves are detected automatically. Simulation results show that the proposed techniques are fast and effective.
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.
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.
Modern databases increasingly integrate new kinds of information, such as multimedia information in the form of image, video, and audio data. Both the dimensionality and the amount of data that need to be processed is...
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ISBN:
(纸本)1581131461
Modern databases increasingly integrate new kinds of information, such as multimedia information in the form of image, video, and audio data. Both the dimensionality and the amount of data that need to be processed is increasing rapidly, increasing the demand for the efficient retrieval of large amounts of multi-dimensional data. Declustering techniques for multi-disk architectures have been effectively used for storage. In this paper, we first establish that besides exploiting the parallelism, a careful organization of each disk must be considered for fast searching. We introduce the notion of page allocation and data space mapping which can be used to organize and retrieve multidimensional data. We develop these notions based on three different partitioning strategies: regular grid partitioning, concentric hypercubes and hyperpyramids.. We develop techniques that satisfy efficient retrieval by optimizing the number of buckets retrieved by the query, disk arm movement and I/O parallelism. We prove that concentric hypercube-based mapping satisfies the optimal clustering and optimal parallelism. We develop a technique based on hyperpyramid partitioning that reduces the number of buckets retrieved by the query and has efficient inter- and intra-disk organizations. We evaluate the performance of proposed techniques by comparing them with the current approaches. The new techniques lead to very significant improvement over the existing techniques, and result in fast retrieval of multi-dimensional data.
With the currently existing shot change detection algorithms, abrupt changes are detected fairly well. It is thus more challenging to detect gradual changes including fades, dissolves, and wipes as these are often mis...
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With the currently existing shot change detection algorithms, abrupt changes are detected fairly well. It is thus more challenging to detect gradual changes including fades, dissolves, and wipes as these are often missed or falsely detected. In this paper, we focus on the detection of wipes. The proposed algorithm begins by processing the visual rhythm, a portion of the DC image sequence. It is a single image, a sub-sampled version of a full video in which the sampling is performed in a pre-determined and in a systematic fashion. The visual rhythm contains distinctive patterns or visual features for many different types of video effects. The different video effects manifest themselves differently on the visual rhythm. In particular, wipes appear as curves that run from the top to the bottom of the visual rhythm. Thus, using the visual rhythm, it becomes possible to automatically detect wipes simply by determining various lines and curves on the visual rhythm.
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