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Efficient Bitmap-based Indexing and Retrieval of Similarity Search Image Queries

机译:基于比特图的索引和检索相似性搜索图像查询

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Finding similar images is a necessary operation in many multimedia applications. Images are often represented and stored as a set of high-dimensional features, which are extracted using localized feature extraction algorithms. Locality Sensitive Hashing is one of the most popular approximate processing techniques for finding similar points in highdimensional spaces. Locality Sensitive Hashing (LSH) and its variants are designed to find similar points, but they are not designed to find objects (such as images, which are made up of a collection of points) efficiently. In this paper, we propose an index structure, Bitmap-Image LSH (bImageLSH), for efficient processing of high-dimensional images. Using a real dataset, we experimentally show the performance benefit of our novel design while keeping the accuracy of the image results high.
机译:发现类似的图像是许多多媒体应用中的必要操作。 通常表示和存储为一组高维特征的图像,其使用本地化特征提取算法提取。 地区敏感散列是最受欢迎的近似处理技术之一,用于在高度空间中找到类似点。 地区敏感散列(LSH)及其变体旨在寻找类似的点,但它们并不设计用于有效地找到对象(例如由点集合的图像)。 在本文中,我们提出了一种索引结构,位图 - 图像LSH(BimagelsH),用于高维图像的有效处理。 使用真实的数据集,我们通过实验显示我们的小说设计的性能优势,同时保持图像的准确性高。

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