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The feature matching is aimed at measuring the difference between two feature vectors, i.e., the feature similarity between an input sketch query and a nature image in the database. In addition, a feature descriptor should also be robust enough to handle the various varieties of image content and be capable of eliminating the ambiguity of sketches caused by the difference in users’ painting skills and styles. In the context of SBIR, an effective feature descriptor can not only work well on natural images but also can be applied to the semantic information extraction of hand-drawn images according to the stroke direction and line continuity of the sketches. feature descriptor that can be fed into subsequent algorithms to perform specific tasks. This is a process of encoding the key information of a natural image or sketch image into a feature vector, a.k.a. Such a technique not only provides users with a convenient and intuitive way to formulate a query but narrows the semantic gap between the query and target images and thus is gaining increasing attention in the image retrieval community.ĭespite the ease and flexibility of interaction of SBIR, there remain two essential factors that could have a significant impact on its practicality and accuracy of retrieval. Compared to other methodologies, SBIR allows users to retrieve relevant images by drawing a sketch image of their desired object/scene on a touch screen. Recently, the rise of touch screen and its associated human-computer interaction technology make it possible for sketch-based image retrieval (SBIR). In contrast to keyword-based methods, CBIR is capable of achieving better retrieval performance by leveraging features such as colors, texture, shape, and spatial relation. Among them, content-based image retrieval (CBIR) approach has emerged as an effective solution to address the challenge. To alleviate the suffering, a considerable amount of effort has been devoted to the development of powerful image retrieval systems.
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Especially with the current explosion in imaging technologies and the wide availability of affordable imaging devices, the number of images has been soaring at an unprecedented rate on the Internet, and this is posing a very tough challenge: how to retrieve the content of interest from a huge collection of images both efficiently and effectively. Over the past decades, digital image as one of the most common media has permeated almost every aspect of our lives. The experimental results indicate that the proposed method is superior to existing peer SBIR systems in terms of retrieval accuracy. To examine the efficiency of our method, we carry out extensive experiments on the public Flickr15K dataset. In addition, we integrate the directional distribution of the barycenters of all sampling points into the feature descriptor and thus improve its representational capability in capturing the semantic information of contours.
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Secondly, we devise a hybrid barycentric feature descriptor (RSB-HOG) that extracts HOG features by randomly sampling points on the edges of a sketch. Firstly, we propose a Gaussian blur-based multiscale edge extraction (GBME) algorithm to capture more comprehensive and detailed features by continuously superimposing the edge filtering results after Gaussian blur processing. In this paper, we introduce a novel sketch image edge feature extraction algorithm to tackle the challenges.
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As a consequence, the performance of existing SBIR systems is still far from being satisfactory. Despite its ease and intuitiveness, it is still a nontrivial task to accurately extract and interpret the semantic information from sketches, largely because of the diverse drawing styles of different users. In contrast to keyword-based methods, SBIR allows users to flexibly manifest their information needs into sketches by drawing abstract outlines of an object/scene. Recently, with the wide availability of touch screen devices and their associated human-computer interaction technology, sketch-based image retrieval (SBIR) methods have attracted more and more attention. Although various kinds of techniques like keyword-/content-based methods have been extensively investigated, how to effectively retrieve relevant images from a large-scale database remains a very challenging task.
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With the explosive increase of digital images, intelligent information retrieval systems have become an indispensable tool to facilitate users’ information seeking process.