4,149 research outputs found

    Image Information Distance Analysis and Applications

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    Image similarity or distortion assessment is fundamental to a broad range of applications throughout the field of image processing and machine vision. These include image restoration, denoising, coding, communication, interpolation, registration, fusion, classification and retrieval, as well as object detection, recognition, and tracking. Many existing image similarity measures have been proposed to work with specific types of image distortions (e.g., JPEG compression). There are also methods such as the structural similarity (SSIM) index that are applicable to a wider range of applications. However, even these "general-purpose" methods offer limited scopes in their applications. For example, SSIM does not apply or work properly when significant geometric changes exist between the two images being compared. The theory of Kolmogorov complexity provides solid groundwork for a generic information distance metric between any objects that minorizes all metrics in the class. The Normalized Information Distance (NID) metric provides a more useful framework. While appealing, the challenge lies in the implementation, mainly due to the non-computable nature of Kolmogorov complexity. To overcome this, a Normalized Compression Distance (NCD) measure was proposed, which is an effective approximation of NID and has found successful applications in the fields of bioinformatics, pattern recognition, and natural language processing. Nevertheless, the application of NID for image similarity and distortion analysis is still in its early stage. Several authors have applied the NID framework and the NCD algorithm to image clustering, image distinguishability, content-based image retrieval and video classification problems, but most reporting only moderate success. Moreover, due to their focuses on ! specific applications, the generic property of NID was not fully exploited. In this work, we aim for developing practical solutions for image distortion analysis based on the information distance framework. In particular, we propose two practical approaches to approximate NID for image similarity and distortion analysis. In the first approach, the shortest program that converts one image to another is found from a list of available transformations and a generic image similarity measure is built on computing the length of this shortest program as an approximation of the conditional Kolmogorov complexity in NID. In the second method, the complexity of the objects is approximated using Shannon entropy. Specifically we transform the reference and distorted images into wavelet domain and assume local independence among image subbands. Inspired by the Visual Information Fidelity (VIF) approach, the Gaussian Scale Mixture (GSM) model is adopted for Natural Scene Statistics (NSS) of the images to simplify the entropy computation. When applying image information distance framework in real-world applications, we find information distance measures often lead to useful features in many image processing applications. In particular, we develop a photo retouching distortion measure based on training a Gaussian kernel Support Vector Regression (SVR) model using information theoretic features extracted from a database of original and edited images. It is shown that the proposed measure is well correlated with subjective ranking of the images. Moreover, we propose a tone mapping operator parameter selection scheme for High Dynamic Range (HDR) images. The scheme attempts to find tone mapping parameters that minimize the NID of the HDR image and the resulting Low Dynamic Range (LDR) image, and thereby minimize the information loss in HDR to LDR tone mapping. The resulting images created by minimizing NID exhibit enhanced image quality

    Reducing the loss of information through annealing text distortion

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    Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. Granados, A. ;Cebrian, M. ; Camacho, D. ; de Borja Rodriguez, F. "Reducing the Loss of Information through Annealing Text Distortion". IEEE Transactions on Knowledge and Data Engineering, vol. 23, no. 7 pp. 1090 - 1102, July 2011Compression distances have been widely used in knowledge discovery and data mining. They are parameter-free, widely applicable, and very effective in several domains. However, little has been done to interpret their results or to explain their behavior. In this paper, we take a step toward understanding compression distances by performing an experimental evaluation of the impact of several kinds of information distortion on compression-based text clustering. We show how progressively removing words in such a way that the complexity of a document is slowly reduced helps the compression-based text clustering and improves its accuracy. In fact, we show how the nondistorted text clustering can be improved by means of annealing text distortion. The experimental results shown in this paper are consistent using different data sets, and different compression algorithms belonging to the most important compression families: Lempel-Ziv, Statistical and Block-Sorting.This work was supported by the Spanish Ministry of Education and Science under TIN2010-19872 and TIN2010-19607 projects

    IDENTIFICATION OF COVER SONGS USING INFORMATION THEORETIC MEASURES OF SIMILARITY

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    13 pages, 5 figures, 4 tables. v3: Accepted version13 pages, 5 figures, 4 tables. v3: Accepted version13 pages, 5 figures, 4 tables. v3: Accepted versio

    Plant image retrieval using color, shape and texture features

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    We present a content-based image retrieval system for plant image retrieval, intended especially for the house plant identification problem. A plant image consists of a collection of overlapping leaves and possibly flowers, which makes the problem challenging.We studied the suitability of various well-known color, shape and texture features for this problem, as well as introducing some new texture matching techniques and shape features. Feature extraction is applied after segmenting the plant region from the background using the max-flow min-cut technique. Results on a database of 380 plant images belonging to 78 different types of plants show promise of the proposed new techniques and the overall system: in 55% of the queries, the correct plant image is retrieved among the top-15 results. Furthermore, the accuracy goes up to 73% when a 132-image subset of well-segmented plant images are considered

    Further results on dissimilarity spaces for hyperspectral images RF-CBIR

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    Content-Based Image Retrieval (CBIR) systems are powerful search tools in image databases that have been little applied to hyperspectral images. Relevance feedback (RF) is an iterative process that uses machine learning techniques and user's feedback to improve the CBIR systems performance. We pursued to expand previous research in hyperspectral CBIR systems built on dissimilarity functions defined either on spectral and spatial features extracted by spectral unmixing techniques, or on dictionaries extracted by dictionary-based compressors. These dissimilarity functions were not suitable for direct application in common machine learning techniques. We propose to use a RF general approach based on dissimilarity spaces which is more appropriate for the application of machine learning algorithms to the hyperspectral RF-CBIR. We validate the proposed RF method for hyperspectral CBIR systems over a real hyperspectral dataset.Comment: In Pattern Recognition Letters (2013

    Adaptive Nonparametric Image Parsing

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    In this paper, we present an adaptive nonparametric solution to the image parsing task, namely annotating each image pixel with its corresponding category label. For a given test image, first, a locality-aware retrieval set is extracted from the training data based on super-pixel matching similarities, which are augmented with feature extraction for better differentiation of local super-pixels. Then, the category of each super-pixel is initialized by the majority vote of the kk-nearest-neighbor super-pixels in the retrieval set. Instead of fixing kk as in traditional non-parametric approaches, here we propose a novel adaptive nonparametric approach which determines the sample-specific k for each test image. In particular, kk is adaptively set to be the number of the fewest nearest super-pixels which the images in the retrieval set can use to get the best category prediction. Finally, the initial super-pixel labels are further refined by contextual smoothing. Extensive experiments on challenging datasets demonstrate the superiority of the new solution over other state-of-the-art nonparametric solutions.Comment: 11 page

    A video compression-based approach to measure music structural similarity

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    International audienceThe choice of the distance measure between time-series representations can be decisive to achieve good classification results in many content-based information retrieval applications. In the field of Music Information Retrieval, two-dimensional representations of the music signal are ubiquitous. Such representations are useful to display patterns of evidence that are not clearly revealed directly in the time domain. Among these representations, self-similarity matrices have become common representations for visualizing the time structure of an audio signal. In the context of organizing recordings, recent work has shown that, given a collection of recordings, it is possible to to group performances of the same musical work based on the pairwise similarity between structural representations of the audio signal. In this work, we introduce the use of the Campana- Keogh distance, a video compression-based measure, to compare musical items based on their structure. Through extensive experiments, we show that the use of this distance measure outperforms the results of previous work using similar approaches but other distance measures. Along with quantitative results, detailed examples are provided to to illustrate the benefits of using the newly proposed distance measure

    Analysis and study on text representation to improve the accuracy of the Normalized Compression Distance

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    The huge amount of information stored in text form makes methods that deal with texts really interesting. This thesis focuses on dealing with texts using compression distances. More specifically, the thesis takes a small step towards understanding both the nature of texts and the nature of compression distances. Broadly speaking, the way in which this is done is exploring the effects that several distortion techniques have on one of the most successful distances in the family of compression distances, the Normalized Compression Distance -NCD-.Comment: PhD Thesis; 202 page

    A fast compression-based similarity measure with applications to content-based image retrieval

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    Compression-based similarity measures are effectively employed in applications on diverse data types with a basically parameter-free approach. Nevertheless, there are problems in applying these techniques to medium-to-large datasets which have been seldom addressed. This paper proposes a similarity measure based on compression with dictionaries, the Fast Compression Distance (FCD), which reduces the complexity of these methods, without degradations in performance. On its basis a content-based color image retrieval system is defined, which can be compared to state-of-the-art methods based on invariant color features. Through the FCD a better understanding of compression-based techniques is achieved, by performing experiments on datasets which are larger than the ones analyzed so far in literature
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