137 resultados para Content-based image retrieval


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In this paper, we propose to complement a region descriptor for shape retrieval with a contour descriptor; the descriptor thus obtained is a composite descriptor. We use a combination of a region descriptor and contour descriptor to obtain the proposed descriptor. A contour extraction technique is also proposed; it is required for extracting the contour of the image prior to applying a contour descriptor. Normalization of descriptors is required before disparate descriptors can be combined into a single descriptor. Hence, we test the performance of two common methods for normalization of descriptors and adopt the better performing method. Experiments are performed to test the effectiveness of the proposed descriptor for retrieval of 2d images. Sets of composite descriptors, obtained by assigning different weights to the region component and the contour component, are also evaluated. Item S8 within the MPEG-7 Still Images Content Set is used for performing experiments; this dataset consists of 3621 still images. Experimental results show that the proposed descriptor is effective.

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In this paper, a composite descriptor for shape retrieval is proposed. The composite descriptor is obtained based upon corner-points and shape region. In an earlier paper, we proposed a composite descriptor based on shape region and shape contour, however, the descriptor was not effective for all perspective and geometric transformations. Hence, we modify the composite descriptor by replacing contour features with corner-points features. The proposed descriptor is obtained from Generic FourierDescriptors (GFD) of the shape region and the GFD ofthe corner-points. We study the performance of the proposed composite descriptor. The proposed method is evaluated using Item S8 within the MPEG-7 Still Images Content Set. Experimental results show that the proposed descriptor is effective.

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In this thesis, the author designed three sets of preference based ranking algorithms for information retrieval and provided the corresponsive applications for the algorithms. The main goal is to retrieve recommended, high similar and valuable ranking results to users.

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The goal of this research is to develop a computer aided diagnostic (CAD) system that can detect breast cancer in the early stage by using microarray and image data. We verified the performance of six well known classification algorithms with various performance matrices. Although we do not suggest a unique classifier algorithm for a CAD system, we do identify a number of algorithms whose performance is very promising. The algorithms performance was validated by 3 images dataset; two have been used for the first time in this experiment. Multidimensional image filtering is adopted for the final data extraction. The image data classification performance is compared with microarray data. Results suggest the most effective means of breast cancer identification in the early stage is a hybrid approach.

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Conductivities greater than or equal to 10−8 S cm−1 at Tg are reported in polymer electrolytes based on lithium triflate salt and a series of polymers whose Tg is greater than 90°C. The highest conductivities were observed for poly(acrylonitrile) based systems with salt concentrations greater than 60 wt.%. The conductivity in all cases investigated increases with increasing salt concentration. 1H-NMR T2 relaxation measurements suggest that Tg decreases with increasing salt content and confirms that these materials are glassy at room temperature and hence that the conductivity is significantly decoupled from the structural relaxations. It appears that the nature of the polymer is important in determining the level of ionic conductivity, possibly due to differences in polymer coordinating ability or differences in Tg. Polymer-in-salt mixtures based on a tetra-alkyl ammonium imide molten salt and several high Tg polymers are also reported. The conductivities of these mixtures appear to be independent of the polymer type.

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Best practice in obesity prevention has generally been defined in terms of ‘what’ needs to be done while neglecting ‘how’. A multifaceted definition of best practice, which combines available evidence on what actions to take, with an established process for interpreting this information in a specific community context, provides a more appropriate basis for defining the principles of best practice in community-based obesity prevention. Based on analysis of a range of literature, a preliminary set of principles was drafted and progressively revised through further analyses of published literature and a series of consultations. The framework for best practice principles comprises: community engagement, programme design and planning, evaluation, implementation and sustainability, and governance. Specific principles were formulated within this framework. While many principles were generic, distinctive features of obesity prevention were also covered. The engagement of end-users influenced the design of the formatting of the outputs, which represent three levels of knowledge transfer: detailed evidence summaries, guiding questions for programme planners and a briefer set of questions for simpler communication purposes. The best practice principles provide a valuable mechanism for the translation of existing evidence and experience into the decision-making processes for planning, implementing and evaluating the complex community-based interventions needed for successful obesity prevention.

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Various issues related to the multimedia information retrieval and media access are discussed. The feasible solutions for automatic signal-based analysis of media content are analyzed. The extent of user involvement in the content creation process is emphasized. The applications driving the creation and usage of context and metadata are also elaborated.

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Composing a multimedia presentation may require creation or generation of suitable images and video segments, as well as animation, sound, or special effects. Obtaining images or video sequences can be prohibitively expensive when costs of travel to location, equipment, staff, etc, are considered. Those problems can be alleviated with the use of pictorial and video digital libraries, such libraries require methods for comprehensive indexing and annotation of stored items and efficient retrieval tools.

We propose a system based on user oriented perceptions as they influence query formation in image and video retrieval. We present a method based on user dependent conceptual structures for creating and maintaining indexes to images and video sequences.