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In this paper we study the problem of content-based image retrieval. In this problem, the most popular performance measure is the top precision measure, and the most important component of a retrieval system is the similarity function used to compare a query image against a database image. However, up to now, there is no existing similarity learning method proposed to optimize the top precision measure...
Given a query image, retrieving images depicting the same object in a large scale database is becoming an urgent and challenging task. Recently, Compact Description for Visual Search (CDVS) is drafted by the ISO/IEC Moving Pictures Experts Group (MPEG) to support image retrieval applications, and it has been published as an international standard. Unfortunately, with regard to applications with hugely...
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...
Finding matching images across large datasets plays a key role in many computer vision applications such as structure-from-motion (SfM), multi-view 3D reconstruction, image retrieval, and image-based localisation. In this paper, we propose finding matching and non-matching pairs of images by representing them with neural network based feature vectors, whose similarity is measured by Euclidean distance...
Region-based Image Retrieval (RBIR), which bases itself on image segmentation rather than global features or key-point-based local features, is a branch of Content-based Image Retrieval. This paper proposes a novel RBIR-oriented image segmentation algorithm named Edge Integrated Minimum Spanning Tree (EI-MST). The difference between EI-MST and the traditional MST-based methods is that EI-MST generates...
The scientific problem of real-time camera-based document image retrieval is achieved by computing the image features adapted to this acquisition mode i.e. the image features which are highly discriminative even under challenging conditions of camera capture as well as which are light to be computed. In this paper, we propose new extension features to our previously proposed SRIF descriptor. The new...
Content-Based Medical Image Retrieval (CBMIR) is an important research field in the context of medical data management. In this paper we propose a novel CBMIR system for the automatic retrieval of radiographic images. Our approach employs a Convolutional Neural Network (CNN) to obtain high-level image representations that enable a coarse retrieval of images that are in correspondence to a query image...
In this paper a novel CNN-based approach in the Content Based Image Retrieval domain that exploits supervised learning is proposed. We employ a deep CNN model to derive feature representations from the activations of the deepest layers and we refine the weights of the utilized layers in order to produce better image descriptors using information obtained from the available data labels. To this end,...
Using content-based binary codes to tag digital images has emerged as a promising retrieval technology. Recently, Radon barcodes (RBCs) have been introduced as a new binary descriptor for image search. RBCs are generated by binarization of Radon projections and by assembling them into a vector, namely the barcode. A simple local thresholding has been suggested for binarization. In this paper, we put...
This paper presents a novel image feature representation method, called multi-channel micro-structure difference descriptor (MCMSDD) for image retrieval. With the local feature extraction from a micro-structure and MAX operator, MCMSDD integrates the advantages of multi-channel local binary encoding and color difference histogram , which are the fusion of color, texture and spatial distribution information...
Geometric verification with epipolar geometry often results in a high score for an incorrect image pair due to ambiguity in its geometric constraints. The ambiguity is caused by a high degree of freedom in the epipolar geometry and a weak constraint from the fitting between a point and a line. In order to mitigate the ambiguity, we propose to filter geometrically inconsistent components, namely correspondences,...
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