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In this paper, we present an out-of-sample extrapolation (OSE) scheme in the context of semi-supervised manifold learning (OSESSL). Manifold learning (ML) takes samples with high dimensionality and learns a set of low dimensional embeddings. Embeddings generated by ML preserve nonlinear relationships between samples allowing dataset visualization, classification, or evaluation of object similarity...
The Color and texture information have been the primitive image descriptors in content based image retrieval systems. This work describes an image retrieval method which uses color and texture approach for feature extraction. An image is represented by a set of regions, roughly corresponding to objects, which are characterized by color and texture. For segmenting images, JSEG (J-Segmentation) algorithm...
This work describes the attribute evaluation sections of the ambitious goal of creating a large-scale content-based image retrieval (CBIR) system for solar phenomena in NASA images from the Solar Dynamics Observatory mission. This mission, with its Atmospheric Imaging Assembly (AIA), is generating eight 4096 pixels × 4096 pixels images every 10 seconds, leading to a data transmission rate of approximately...
Automatic image annotation or image classification can be an important step when searching for images from a database. Common approaches to medical image annotation with the Image Retrieval for Medical Applications (IRMA) code make poor or no use of its hierarchical nature, where different dense sampled pixel based information methods outperform global image descriptors. In this work we address the...
In this paper, we propose a content-based image retrieval system based on an efficient combination of both color and texture features. According to HSV (Hue, Saturation, and Value) color space, we quantified the color space into non-equal intervals, and then construct a one dimensional feature vector and represented the color feature. Similarly, the work of texture feature extraction is obtained by...
Content-based means that the search makes use of the contents of the images themselves, rather than relying on human inputted metadata such as captions or keywords. By content-based techniques, a user can specify contents of interest in a query. The contents may be colors, textures, shapes, or the spatial layout of target images. We propose a CBIR system which is implemented with the help of combination...
In this paper, we have investigated the capabilities of 4 approaches for image search for a CBIR system. First two approaches are based on comparing the images using color histograms of RGB and HSV spaces, respectively. The other 2 approaches are based on two quantitative image fidelity measurements, Mean Square Error (MSE) and Structural Similarity Index (SSIM), which provide a degree of similarity...
Semantic gap, difference between visual features and semantic annotations, is an important problem of Content-Based Image Retrieval (CBIR) systems. In this study, a new Content-Based Image Retrieval system is proposed by using Visual Attention which is a part of human visual system. In the proposed work, the region of interests are extracted by using Itti-Koch visual attention model. The attention...
Over the past few years, Content-based image retrieval (CBIR) has been an active research area. A rapid proliferation has been witnessed in the fields of both theoretical research and development of the CBIR system. The most commonly used transformation techniques in CBIR include wavelet and Fourier transformations; in spite of their widespread utilization, they have not been very effective in representing...
An image is often represented by a set of local invariant features for many computer vision tasks such as object recognition and content-based image retrieval (CBIR), in which correct and reliable feature matching is an essential and challenging issue. Aiming at the problem of the existence of the false matches in CBIR system, we put forward a post-verification method in this paper where RANSAC algorithm...
The appearance and popularization of the computer makes the global e-commerce overwhelming tide. CBIR-based system has been used in all kinds of applications, however, the system is seldom applied to the aspect of clothing electronic retailing. The paper describes a method for enhancing the search and retrieval of product information, discussing content-based image retrieval in the application of...
The detection of texts in video images is an important task towards automatic content-based information indexing and retrieval system. In this paper, we propose a texture-based method for text detection in complex video images. Taking advantage of the desirable characteristic of gray-scale invariance of local binary patterns (LBP), we apply a modified LBP operator to extract feature of texts. A polynomial...
To efficiently deal with the curse of dimensionality in the content-based image retrieval (CBIR) system, a novel image retrieval algorithm is proposed by combination of local discriminant embedding (LDE) and least square SVM (LS-SVM) in this paper. LDE aims to achieve good discriminating performance by integrating the local geometrical structure and class relations between image data. LS-SVM classifier...
Research in content-based image retrieval (CBIR) shows that high-level semantic concepts in image cannot be constantly depicted using low-level image features. So the process of designing a CBIR system should take into account diminishing the existing gap between low-level visual image features and the high-level semantic concepts. In this paper, we propose a new architecture for a CBIR system named...
Content-based image retrieval (CBIR) is an effective approach for obtaining desired image, however, due to the semantic gap between low-level visual features and high-level concept of image, CBIR system of state-of-the-art always can't achieve satisfying retrieval performance. In this paper, we propose a novel CBIR system framework. In order to bridge the semantic gap, the mechanism of relevance feedback...
Content based image retrieval (CBIR) refers to the ability to retrieve images on the basis of image content. In our work, we describe an approach to CBIR for various database images that relies on human input machine learning and computer vision. More specifically we apply expert level human interaction for solving that aspect of the problem and we employ machine learning algorithms to allow the system...
Content based image retrieval (CBIR) systems aim to provide a means to find pictures in large repositories without using any other information except its contents usually as low-level descriptors. Since these descriptors do not exactly match the high level semantics of the image, assessing perceptual similarity between two pictures using only their feature vectors is not a trivial task. In fact, the...
This paper presents a novel similarity measure method based a combination of color and texture feature representations. In this method, the YIQ color space is chosen, because it can describe both color images and gray images and the transform from RGB to YIQ is linear and simple than other color space. In the proposed method, we firstly segment image using texture feature by combination of wavelet...
This paper provides a content-based digital image retrieval system. Our CBIR system uses the query by example technique and the relevance feedback. A Gabor filter based image feature extraction is proposed first. Thus, 3D image feature vectors using even-symmetric 2D Gabor filters are computed for the images of a large collection and for the input image. At each step an input image is selected, from...
In this paper, we present a content-based image retrieval (CBIR) system called MammoSVD. This CBIR system is developed based on breast density - fatty or dense, and the database used, from the IRMA project, provides images with the ground truth already set. Singular value decomposition (SVD) is proposed for the breast density characterization by the selection of the first singular values, in order...
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