This paper presents a novel relational database architecture aimed to visual objects classification and retrieval. The framework is based on the bag-of-features image representation model combined with the Support Vec...
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ISBN:
(纸本)9781479983223
This paper presents a novel relational database architecture aimed to visual objects classification and retrieval. The framework is based on the bag-of-features image representation model combined with the Support Vector Machine classification and is integrated in a Microsoft SQL Server database.
One of the key unresolved issues of imageprocessing is the lack of methods for searching images similar to the reference image. This paper focuses on objects that there are in images and presents a method to compare ...
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ISBN:
(纸本)9783642552243
One of the key unresolved issues of imageprocessing is the lack of methods for searching images similar to the reference image. This paper focuses on objects that there are in images and presents a method to compare the objects and search for images that contain objects belonging to the same classes. Taking advantage of the fact that local keypoints of images constitute a very good basis for further processingimages, we use them for objects comparison. More precisely, the comparison of images is based on histograms, that are generated on the basis of the keypoints of objects contained in images. We present results of experiments which have been conducted for various classes of objects and histograms generated using the proposed method.
Segmentation of digital images is an important issue of object recognition. This method of imageprocessing allows to determine single object areas in images. This paper presents an improved segmentation method which ...
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ISBN:
(纸本)9783642552243
Segmentation of digital images is an important issue of object recognition. This method of imageprocessing allows to determine single object areas in images. This paper presents an improved segmentation method which gives a possibility to detect single objects in images by using the disparity map algorithm in connection with the mean shift pixel grouping algorithm. images are processed in grayscale where range of colors is in from 0 to 255. Grayscale allows to detect objects on the basis of pixels brightness. To achieve this purpose we used one of grouping algorithms known as mean shift. images obtained from mean shift are in the form of separated images which could be subject of further processing. Important feature of mean shift processing is that we obtain the results in the form of backgroundless images containing important objects from the input image.
Modeling user preferences in photographic images is often reduced to analyzing intermediate explicit representations (e.g. textual tags) as means of capturing the objective and subjective properties of image perceptio...
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ISBN:
(纸本)9781479923410
Modeling user preferences in photographic images is often reduced to analyzing intermediate explicit representations (e.g. textual tags) as means of capturing the objective and subjective properties of image perception, trying to distill the essence of what gives pleasure. We propose an alternative approach that bypasses the necessity to build an explicit conceptual coding of image preferences, operating directly on the raw properties of the images, extracted with heterogeneous feature descriptors. This is achieved through the counting grid model, which fuses together content-based and aesthetics themes into a 2D map in an unsupervised way. We show that certain locations in this map correspond to perceptually intuitive image classes, even without relying on tags or other user-defined information. Moreover, we show that users' individual preferences can be represented as distributions over the map, allowing us to evaluate the affinity between different users' appreciations. We experiment on a large Flickr dataset, clustering users by affinity, and validating these clusters by checking users that belong to the same Flickr photo groups.
Modeling user preferences in photographic images is often reduced to analyzing intermediate explicit representations (e.g. textual tags) as means of capturing the objective and subjective properties of image perceptio...
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ISBN:
(纸本)9781479923427
Modeling user preferences in photographic images is often reduced to analyzing intermediate explicit representations (e.g. textual tags) as means of capturing the objective and subjective properties of image perception, trying to distill the essence of what gives pleasure. We propose an alternative approach that bypasses the necessity to build an explicit conceptual coding of image preferences, operating directly on the raw properties of the images, extracted with heterogeneous feature descriptors. This is achieved through the counting grid model, which fuses together content-based and aesthetics themes into a 2D map in an unsupervised way. We show that certain locations in this map correspond to perceptually intuitive image classes, even without relying on tags or other user-defined information. Moreover, we show that users' individual preferences can be represented as distributions over the map, allowing us to evaluate the affinity between different users' appreciations. We experiment on a large Flickr dataset, clustering users by affinity, and validating these clusters by checking users that belong to the same Flickr photo groups.
This paper presents a novel power-aware motion estimation algorithm, called adaptive content-based subsample algorithm (ACSA), for battery-powered multimedia devices. While the battery status changes, the architecture...
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This paper presents a novel power-aware motion estimation algorithm, called adaptive content-based subsample algorithm (ACSA), for battery-powered multimedia devices. While the battery status changes, the architecture adaptively performs graceful tradeoffs between power consumption and compression quality. As the available energy decreases, the algorithm raises the subsample rate for maximizing battery lifetime. Differing from the existing subsample algorithms, the content-based algorithm first extracts edge pixels from a macro-block and then subsamples the remaining low-frequency part. In this way, we can alleviate the aliasing problem and thus keep the quality degradation low as the subsample rate increases. As shown in experimental results, the architecture can dynamically operate at different power consumption modes with little quality degradation according to the remaining capacity of battery pack while the power overhead of edge extraction is under 0.8%.
Multiple description coding has been studied as an approach for transmission of images over error Prone environments. The multiple description coding method proposed here takes into account the content of the image an...
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ISBN:
(纸本)076951507X
Multiple description coding has been studied as an approach for transmission of images over error Prone environments. The multiple description coding method proposed here takes into account the content of the image and provides the least amount of degradation, caused by loss of descriptors, for those areas of the image which are of greater interest. This is achieved by employing a non-linear geometrical transform to odd redundancy mainly to the area of interest followed by a partitioning of the nonlinearly transformed image into sub-images which are coded and transmitted over separate channels, Simulations show that this approach yields exceptional performance even when only one descriptor is received. Moreover, the method proposed here can be implemented through pre- and post-processing of the image data, without modification to the source codecs (e.g., JPEG).
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