This paper presents a simple and effective pre-processing method developed for the segmentation of MPEG compressed video sequences. The proposed method for scene-cut detection only involves computing the number of bit...
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This paper presents a simple and effective pre-processing method developed for the segmentation of MPEG compressed video sequences. The proposed method for scene-cut detection only involves computing the number of bits spent for each frame (encoding cost data), thus avoiding decoding the bitstream. The information is separated into I-, P-, B-frames, thus forming 3 vectors which are independently processed by a new peak detection algorithm based on overcomplete filter banks and on joint thresholding using a confidence number. Each processed vector yields a set of candidate frame numbers, i.e. 'hints' of positions where scene-cuts may have occurred. The 'hints' for all frame types are recombined into one frame sequence and clustered into scene cuts. The algorithm was not designed to distintuish among types of cuts but rather to indicate its position and duration. Experimental results show that the proposed algorithm is effective in detecting abrupt scene changes as well as gradual transitions. For precision demanding applications, the algorithm can be used with a low confidence factor just to select the frames that are worth being investigated by a more complex algorithm. The algorithm is not particularly tailored to MPEG and can be applied to most video compression techniques.
The aim of this report is to be controversial and to engage a debate within the research community. Issues of whether some of the work in image and videodatabases has been directed at solutions in search of a problem...
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The aim of this report is to be controversial and to engage a debate within the research community. Issues of whether some of the work in image and videodatabases has been directed at solutions in search of a problem are discussed. Important applications in the area of media-based digital libraries that will enhance human experience are also detailed.
Color indexing is a technique by which images in the database could be retrieved on the bases of their color content. In this paper, we propose a new set of color features for representing color images, and show how t...
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Color indexing is a technique by which images in the database could be retrieved on the bases of their color content. In this paper, we propose a new set of color features for representing color images, and show how they can be computed and used efficiently to retrieve images that possess certain similarity. These features are based on the first three moments of each color channel. Two differences distinguish this work from previous work reported in the literature. First, we compute the third moment of the color channel distribution around the second moment not around the first moment. The second moment is less sensitive to small luminance changes, than the first moment. Second we combine all three moment values in a single descriptor. This reduces the number of floating point values needed to index the image and hence speeds up the search. To give the user flexibility in terms of defining his center of attention during query time, the proposed approach divides the image into five geometrical regions and allows the user to give different weights for each region to designate its importance. The approach has been tested on databases of 205 images of airplanes and natural scenes. It proved to be insensitive to small rotations and small translations in the image and yielded a better hit rate than similar algorithms previously reported in the literature.
A scheme that reduces the needed storage considerably without reducing the retrieval effectiveness of a content-based imageretrieval (CBIR) system such as ASSERT is introduced. This scheme enables ASSERT to retrieve ...
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A scheme that reduces the needed storage considerably without reducing the retrieval effectiveness of a content-based imageretrieval (CBIR) system such as ASSERT is introduced. This scheme enables ASSERT to retrieve the most similar physician-marked key images from the other patients, and those adjacent images that cohere with the key images. Only the key images and the other images that cohere with the key images are retained for archival purposes. This significantly reduces the amount of storage needed for fast retrieval.
A highly integrated wavelet-based image management system is proposed. Three solutions for key aspect of image management are derived: content-based imageretrieval (CBIR);image compression/decompression;and image tra...
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A highly integrated wavelet-based image management system is proposed. Three solutions for key aspect of image management are derived: content-based imageretrieval (CBIR);image compression/decompression;and image transmission. By exploring the excellent features of wavelet, integrating key aspect of image management, the system shows a high overall performance.
Enabling the efficient storage, access and retrieval of large volumes of multi-dimensional data is one of the important emerging problems in databases. We present a framework for adaptively storing, accessing, and ret...
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Enabling the efficient storage, access and retrieval of large volumes of multi-dimensional data is one of the important emerging problems in databases. We present a framework for adaptively storing, accessing, and retrieving large images. The framework uses a space and frequency graph to generate and select image view elements for storing in the database. By adapting to user access patterns, the system selects and stores those view elements that yield the lowest average cost for accessing the multi-resolution sub-region image views. The system uses a second adaptation strategy to divide computation between server and client in progressive retrieval of image views using view elements. We show that the system speeds-up retrieval for access and retrieval modes such as drill-down browsing and remote zooming and panning and minimizes the amount of data transfer over the network.
With the advancement of multimedia technology and the internet, numerous applications have arisen which require the storage and retrieval of large image and videodatabases. A novel method (Eigenwavelet) was developed...
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ISBN:
(纸本)0819432997
With the advancement of multimedia technology and the internet, numerous applications have arisen which require the storage and retrieval of large image and videodatabases. A novel method (Eigenwavelet) was developed to retrieve images from a large heterogeneous image database upon a user-specified query. The queries are in the form of an image(s) that the user seeks to find similar matches to in the database. Using the queries, an efficient algorithm was developed which decomposed each image in the database using wavelet packet analysis. Along each node of the packet tree, Principal Component Analysis (PCA) was applied to the: database images after wavelet packet decomposition, and a set of eigenvectors were generated for each node of the packet tree. To search the image database, the query images are projected onto these eigenvectors (Eigenwavelet coefficients). A distance metric is computed between the projections of the queries and the projections of the images in the database onto the eigenwavelets. Those images with minimal distance (L1) are retrieved in response to a unique query set. Simulations with a heterogeneous image database suggest the Eigenwavelet method of imageretrieval is a robust and computationally tractable method of retrieving images with a probability of detection > .8.
A new system, the so-called MUVIS, is introduced for content-based indexing and retrieval for image database management systems. In addition to traditional indexing by key words, MUVIS allows indexing of objects and i...
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A new system, the so-called MUVIS, is introduced for content-based indexing and retrieval for image database management systems. In addition to traditional indexing by key words, MUVIS allows indexing of objects and images based on color, texture, shape and objects layout inside them. Due to the use of large vector features, the pyramid trees are employed to create the index structure.
The detection of shot boundaries in video sequences is an important task for generating indexed videodatabases. This paper provides a comprehensive quantitative comparison of the metrics that have been applied to sho...
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The detection of shot boundaries in video sequences is an important task for generating indexed videodatabases. This paper provides a comprehensive quantitative comparison of the metrics that have been applied to shot boundary detection. In addition, several standardized statistical tests that have not been applied to this problem, and three new metrics, are considered. A mathematical framework for quantitatively comparing metrics is supplied. Experimental results based on a video database containing 39,000 frames are included.
This paper is concerned with estimating a probability density function of human skin color using a finite Gaussian mixture model whose parameters are estimated through the EM algorithm. Hawkins' statistical test o...
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This paper is concerned with estimating a probability density function of human skin color using a finite Gaussian mixture model whose parameters are estimated through the EM algorithm. Hawkins' statistical test on the normality and homoscedasticity (common covariance matrix) of the estimated Gaussian mixture models is performed and McLachlan's bootstrap method is used to test the number of components in a mixture. Experimental results show that the estimated Gaussian mixture model fits skin images from a large database. Applications of the estimated density function in image and videodatabases are presented.
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