4,389 research outputs found

    Colour appearance descriptors for image browsing and retrieval

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    In this paper, we focus on the development of whole-scene colour appearance descriptors for classification to be used in browsing applications. The descriptors can classify a whole-scene image into various categories of semantically-based colour appearance. Colour appearance is an important feature and has been extensively used in image-analysis, retrieval and classification. By using pre-existing global CIELAB colour histograms, firstly, we try to develop metrics for wholescene colour appearance: “colour strength”, “high/low lightness” and “multicoloured”. Secondly we propose methods using these metrics either alone or combined to classify whole-scene images into five categories of appearance: strong, pastel, dark, pale and multicoloured. Experiments show positive results and that the global colour histogram is actually useful and can be used for whole-scene colour appearance classification. We have also conducted a small-scale human evaluation test on whole-scene colour appearance. The results show, with suitable threshold settings, the proposed methods can describe the whole-scene colour appearance of images close to human classification. The descriptors were tested on thousands of images from various scenes: paintings, natural scenes, objects, photographs and documents. The colour appearance classifications are being integrated into an image browsing system which allows them to also be used to refine browsing

    Colour cluster analysis for pigment identification

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    This paper presents image processing algorithms designed to analyse the colour CIE Lab histogram of high resolution images of paintings. Three algorithms are illustrated which attempt to identify colour clusters, cluster shapes due to shading and finally to identify pigments. Using the image collection and pigment list of the National Gallery London large numbers of images within a restricted period have been classified with a variety of algorithms. The image descriptors produced were also used with suitable comparison metrics to obtain content-based retrieval of the images

    Enhancing timbre model using MFCC and its time derivatives for music similarity estimation

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    One of the popular methods for content-based music similarity estimation is to model timbre with MFCC as a single multivariate Gaussian with full covariance matrix, then use symmetric Kullback-Leibler divergence. From the field of speech recognition, we propose to use the same approach on the MFCCs’ time derivatives to enhance the timbre model. The Gaussian models for the delta and acceleration coefficients are used to create their respective distance matrix. The distance matrices are then combined linearly to form a full distance matrix for music similarity estimation. In our experiments on two datasets, our novel approach performs better than using MFCC alone.Moreover, performing genre classification using k-NN showed that the accuracies obtained are already close to the state-of-the-art

    Towards efficient music genre classification using FastMap

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    Automatic genre classification aims to correctly categorize an unknown recording with a music genre. Recent studies use the Kullback-Leibler (KL) divergence to estimate music similarity then perform classification using k-nearest neighbours (k-NN). However, this approach is not practical for large databases. We propose an efficient genre classifier that addresses the scalability problem. It uses a combination of modified FastMap algorithm and KL divergence to return the nearest neighbours then use 1- NN for classification. Our experiments showed that high accuracies are obtained while performing classification in less than 1/20 second per track

    Distributed stream reasoning

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    Stream Reasoning is the combination of reasoning techniques with data streams. In this paper, we present our approach to enable rule-based reasoning on semantic data streams in a distributed manne

    Giving order to image queries

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    Users of image retrieval systems often find it frustrating that the image they are looking for is not ranked near the top of the results they are presented. This paper presents a computational approach for ranking keyworded images in order of relevance to a given keyword. Our approach uses machine learning to attempt to learn what visual features within an image are most related to the keywords, and then provide ranking based on similarity to a visual aggregate. To evaluate the technique, a Web 2.0 application has been developed to obtain a corpus of user-generated ranking information for a given image collection that can be used to evaluate the performance of the ranking algorithm

    REST and Linked Data: a match made for domain driven development?

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    At a first glance there might appear to be an obvious alignment and overlap between the approaches prescribed by REST and Linked Data. On more detailed inspection divergences in scope and applicability present themselves, and for some aspects, incompatibility. In this paper we investigate these similarities and differences and suggest the coupling is worthy of a third look: in combination as a flexible environment in which the developer can focus on domain driven applications

    Semantic Web Integration of Cultural Heritage Sources

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    In this paper, we describe research into the use of ontologies to integrate access to cultural heritage and photographic archives. The use of the CIDOC CRM and CRM Core ontologies are described together with the metadata mapping methodology. A system integrating data from four content providers will be demonstrated
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