5,763 research outputs found

    Image Retrieval Based on Fuzzy Edge and Trum Fuzzy Histogram

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    ABSTRACT In recent years, many image retrieval systems based on color feature like fuzzy color histogram, have been applied in image retrieval systems based on content (CBIR). Most of this methods are not able to determine pixels accurate colors, especially in combined manner, and only determine whole distribution of color factor in image; therefore they are not efficient in image retrieval. We have suggested weight vector factor in trum fuzzy histogram in this paper to remove these problems. But these methods only demonstrate total distribution of color feature in image and do not consider any kind of place data, like relative positions of objects in image. Therefore do not prepare strong techniques for image retrievals with complex place ornament. since the edge pixels are important places in image and determine objects in an image and often similar images have similar backgrounds, we use competitive fuzzy edge finder algorithm which effectively categorizes image pixels into 5 classes ,including 4 edge classes in different directions and 1 background class. after categorizing pixels, feature vector for each class would be determined, that includes Trum fuzzy color histogram and place position. we compared our suggested method to fuzzy histogram method and compound neighborhood fuzzy entropy method with color _place feature, as tests results show high efficiency of our suggested method for image retrievals from COREL database, including 3000 images

    Application of the fuzzy logic in content-based image retrieval

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    This paper imports the fuzzy logic into image retrieval to deal with the vagueness and ambiguity of human judgment of image similarity. Our retrieval system has the following properties: firstly adopting the fuzzy language variables to describe the similarity degree of image features, not the features themselves; secondly making use of the fuzzy inference to instruct the weights assignment among various image features; thirdly expressing the subjectivity of human perceptions by fuzzy rules impliedly; lastly we propose an improvement on the traditional histogram called the Average Area Histogram (AAH) to represent color features. Experimentally we realized a fuzzy logic-based image retrieval system with good retrieval performance.Facultad de Informátic

    An Image Retrieval System Based on the Color Complexity of Images

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    The fuzzy color histogram (FCH) spreads each pixel's total membership value to all histogram bins based on their color similarity. The FCH is insensitive to quantization errors. However, the FCH can state only the global properties of an image rather than the local properties. For example, it cannot depict the color complexity of an image. To characterize the color complexity of an image, this paper presents two image features -- the color variances among adjacent segments (CVAAS) and the color variances of the pixels within an identical segment (CVPWIS). Both features can explain not only the color complexity but also the principal pixel colors of an image. Experimental results show that the CVAAS and CVPWIS based image retrieval systems can provide a high accuracy rate for finding out the database images that satisfy the users' requirement. Moreover, both systems can also resist the scale variances of images as well as the shift and rotation variances of segments in images

    A Sub-block Based Image Retrieval Using Modified Integrated Region Matching

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    This paper proposes a content based image retrieval (CBIR) system using the local colour and texture features of selected image sub-blocks and global colour and shape features of the image. The image sub-blocks are roughly identified by segmenting the image into partitions of different configuration, finding the edge density in each partition using edge thresholding followed by morphological dilation. The colour and texture features of the identified regions are computed from the histograms of the quantized HSV colour space and Gray Level Co- occurrence Matrix (GLCM) respectively. The colour and texture feature vectors is computed for each region. The shape features are computed from the Edge Histogram Descriptor (EHD). A modified Integrated Region Matching (IRM) algorithm is used for finding the minimum distance between the sub-blocks of the query and target image. Experimental results show that the proposed method provides better retrieving result than retrieval using some of the existing methods.Comment: 7 page

    Autoencoding the Retrieval Relevance of Medical Images

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    Content-based image retrieval (CBIR) of medical images is a crucial task that can contribute to a more reliable diagnosis if applied to big data. Recent advances in feature extraction and classification have enormously improved CBIR results for digital images. However, considering the increasing accessibility of big data in medical imaging, we are still in need of reducing both memory requirements and computational expenses of image retrieval systems. This work proposes to exclude the features of image blocks that exhibit a low encoding error when learned by a n/p/nn/p/n autoencoder (p ⁣< ⁣np\!<\!n). We examine the histogram of autoendcoding errors of image blocks for each image class to facilitate the decision which image regions, or roughly what percentage of an image perhaps, shall be declared relevant for the retrieval task. This leads to reduction of feature dimensionality and speeds up the retrieval process. To validate the proposed scheme, we employ local binary patterns (LBP) and support vector machines (SVM) which are both well-established approaches in CBIR research community. As well, we use IRMA dataset with 14,410 x-ray images as test data. The results show that the dimensionality of annotated feature vectors can be reduced by up to 50% resulting in speedups greater than 27% at expense of less than 1% decrease in the accuracy of retrieval when validating the precision and recall of the top 20 hits.Comment: To appear in proceedings of The 5th International Conference on Image Processing Theory, Tools and Applications (IPTA'15), Nov 10-13, 2015, Orleans, Franc

    Visual Information Retrieval in Endoscopic Video Archives

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    In endoscopic procedures, surgeons work with live video streams from the inside of their subjects. A main source for documentation of procedures are still frames from the video, identified and taken during the surgery. However, with growing demands and technical means, the streams are saved to storage servers and the surgeons need to retrieve parts of the videos on demand. In this submission we present a demo application allowing for video retrieval based on visual features and late fusion, which allows surgeons to re-find shots taken during the procedure.Comment: Paper accepted at the IEEE/ACM 13th International Workshop on Content-Based Multimedia Indexing (CBMI) in Prague (Czech Republic) between 10 and 12 June 201

    Fusing MPEG-7 visual descriptors for image classification

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    This paper proposes three content-based image classification techniques based on fusing various low-level MPEG-7 visual descriptors. Fusion is necessary as descriptors would be otherwise incompatible and inappropriate to directly include e.g. in a Euclidean distance. Three approaches are described: A “merging” fusion combined with an SVM classifier, a back-propagation fusion combined with a KNN classifier and a Fuzzy-ART neurofuzzy network. In the latter case, fuzzy rules can be extracted in an effort to bridge the “semantic gap” between the low-level descriptors and the high-level semantics of an image. All networks were evaluated using content from the repository of the aceMedia project1 and more specifically in a beach/urban scene classification problem

    A Novel Scheme for Intelligent Recognition of Pornographic Images

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    Harmful contents are rising in internet day by day and this motivates the essence of more research in fast and reliable obscene and immoral material filtering. Pornographic image recognition is an important component in each filtering system. In this paper, a new approach for detecting pornographic images is introduced. In this approach, two new features are suggested. These two features in combination with other simple traditional features provide decent difference between porn and non-porn images. In addition, we applied fuzzy integral based information fusion to combine MLP (Multi-Layer Perceptron) and NF (Neuro-Fuzzy) outputs. To test the proposed method, performance of system was evaluated over 18354 download images from internet. The attained precision was 93% in TP and 8% in FP on training dataset, and 87% and 5.5% on test dataset. Achieved results verify the performance of proposed system versus other related works
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