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Motif is a promising descriptor to depict the content of image. In this study, two motif-relevant matrices, i.e. a motif average matrix (MAM) and a motif excessive matrix (MEM), are proposed firstly to describe the color and texture features of an image. Subsequently, in the light of the inference of MAM and MEM, a motif matrix (MM) is further proposed to resolve the issues of rotated image retrieval...
Content-based image retrieval is an effective and efficient technique to retrieve images from a big dataset with similar images. To have a robust retrieval system, a proper and accurate classification scheme is required to categorise the information of shape, texture, and colours. In this paper, a deep convolutional neural network is proposed to classify the information of radiology images. Deep networks...
In recent years, with the explosion of digital images on the Web, content-based retrieval has emerged as a significant research area. Shapes, textures, edges and segments may play a key role in describing the content of an image. Radon and Gabor transforms are both powerful techniques that have been widely studied to extract shape-texture-based information. The combined Radon-Gabor features may be...
Skin cancer is one of the most frequent cancers among human beings. Whereas, malignant melanoma is the most aggressive and deadly type of skin cancer, and its incidence has been quickly increasing over the last years. The detection of the malignant melanoma in its early stages with dermoscopic images reduces the mortality considerably, hence this a crucial issue for the dermatologists. However, their...
Information is one of the foremost fact in the prompt world. Within that, text information plays an imperative role and can acquire diverse mold. The natural images that consist of such text information are called scene text images. Semantic information of the image is used for content-based image retrieval, indexing and classification purpose. First stage of text extraction is the text and non-text...
In this paper, we introduce a non-parametric texture similarity measure based on the singular value decomposition of the curvelet coefficients followed by a content-based truncation of the singular values. This measure focuses on images with repeating structures and directional content such as those found in natural texture images. Such textural content is critical for image perception and its similarity...
In this paper, we propose an aggregation scheme of local descriptors that preserves local spatial information. Our method is based on the binary product of similarities of nearby matching pairs of descriptors. The similarities are linearized using a tensor framework. We show our approach can be used with any local descriptors, handcrafted like SIFT, or learned like the outputs of convolutional layers...
In this paper, we present a novel, effective, and efficient approach to image retrieval. Basically, it is a fusion of both global and local features of images, which achieves significantly higher retrieval competency. Initially, the global features of images are determined using polar cosine transforms (PCTs). For local features, we use rotation invariant local binary patterns (RLBP) rather than using...
For more than two decades, research has been performed on content-based image retrieval (CBIR). By combining Radon projections and the support vector machines (SVM), a content-based medical image retrieval method is presented in this work. The proposed approach employs the normalized Radon projections with corresponding image category labels to build an SVM classifier, and the Radon barcode database...
Product image search aims to retrieve similar product images based on a query image. While deep learning based features work well in retrieving images of the same category (e.g. “searching for T-shirts from all the clothing images”), they perform poorly when retrieving variants of images within the same category (e.g. “searching for uniform of Chelsea football club from all T-shirts image”), since...
Recently, Content Based Image Retrieval (CBIR) has received a great attention by researchers. It becomes one of the most interesting topic in computer vision and image processing. CBIR image can be represent by local or global features. The entire image is described in the case of global features by using a novel descriptor called Upper-Lower of Local Binary Pattern (UL-LBP) based on Local Binary...
The rapid growth of different types of images has posed a great challenge for scientific fraternity across the world. For easy access to large number of images, efficient indexing and retrieval is required. The field of Content-Based Image Retrieval (CBIR) attempts to solve this problem. This paper proposes a combination of local and global features for CBIR. Local features are extracted through Scale...
Our research focuses on the question of feature descriptors for robust effective computing, presenting a novel feature representation method-rotation-invariant histograms of oriented gradients (Ri-HOG). Most of the existing HOG techniques are computed on a dense grid of uniformly-spaced cells and use overlapping local contrast of rectangular blocks for normalization. However, we adopt annular spatial...
In recent years, the medical image retrieval play an important role in the field of medical diagnosis. The primary goal is to retrieve accurate matching images from the database. In order to achieve this goal, quite a few methods were used in the past years and some of them can get realistic results. However, after some deep and further experiments, we found that some classic feature descriptors or...
Obtaining robust and efficient rotation-invariant texture features in content-based image retrieval field is a challenging work. We propose three efficient rotation-invariant methods for texture image retrieval using copula model based in the domains of Gabor wavelet (GW) and circularly symmetric GW (CSGW). The proposed copula models use copula function to capture the scale dependence of GW/CSGW for...
Definition and extraction of local features play a very important role in image retrieval (IR), pattern recognition and computer vision. Fast growth of technology today calls for local features to be as compact as possible toward real-time and limited bandwidth applications. In this paper, we study the problem of representing images in a compact way to achieve low bit-rate transmission while maintaining...
The future user needs in the field of Multimedia retrieval is the focus of many research and development activists. It is empirically observed that no single algorithm is efficient in extracting all different types of images like building images, flower images, car images and so on. Hence a thorough analysis of certain color, texture and shape extraction techniques are carried out to identify an efficient...
Objective of our paper is to discuss latest pattern recognition applications, techniques and development. Pattern recognition has been demanding field from many years. We are also discuss driving force behind its swift development, that is pattern recognition is used to give human recognition intelligence to machine which is soul of today's many modern application. It acts as wheel of many techniques...
Recent advances in text detection allow for finnding text regions in natural scenes rather accurately. Global features in content based image retrieval, however, typically do not cover such a high level information. While characteristics of text regions may be reflected by texture or color properties, the respective pixels are not treated in a different way. In this contribution we investigate the...
Huge variety of medicine cures diseases. But unlabeled pills sometimes confuse people, even causing adverse drug events. This paper introduces a high accuracy automatic pill recognition method based on pill imprint which is a main discriminative factor between different pills. To describe the imprint information clearly, we propose a Two-step Sampling Distance Sets (TSDS) descriptor based on Distance...
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