A novel locality-sensitive hashing algorithm for similarity searches on large-scale hyperspectral data

Zhou, Yuan; Liu, Chun; Li, Nan; Li, Minzhen

doi:10.1080/2150704x.2016.1207255

Cited by 8 publications

(4 citation statements)

References 8 publications

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“…The data points which have the same hash key are located in a connecting area of the physical memory and the hash key is used to address the hash bucket. To have an orderly save of the hash keys, a hash table is used [26]. When more than one set of hash functions is generated, LSH usually uses more than one hash table.…”

Section: Locality-sensitive Hashingmentioning

confidence: 99%

Document Image Retrieval Based on Visual Saliency Maps

Alaei

Pal

et al. 2019

2019 International Conference on Document Analysis and Recognition Workshops (ICDARW)

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There has been a massive growth in the production of various unstructured, complex and multilingual digitised documents in recent years. Storing and manipulating such digitised documents towards a paperless society has been the objective of emerging technology. As the human visual system can easily distinguish the global summary of images, extracting features based on human attention from images is desirable to achieve more accurate document image retrieval results. Thus, in this research work, an appearance-based document image retrieval system using image saliency maps depending on human visual attention is proposed. The saliency map obtained from the input document image is used to generate a weighted document image. Features are then extracted from the weighted document images using the Gist operator. Then, locality-sensitive hashing is considered to compute similarity distances between a query and the document images in the knowledge-based database. To evaluate the performance of the proposed document image retrieval system MTDB, ITESOFT, and CLEF-IP datasets of document images were used for experimentation. The proposed document image retrieval system provided promising retrieval results compared to the results reported in the literature.

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Section: Locality-sensitive Hashingmentioning

confidence: 99%

Document Image Retrieval Based on Visual Saliency Maps

Alaei

Pal

et al. 2019

2019 International Conference on Document Analysis and Recognition Workshops (ICDARW)

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“…So, the hash key is used to address the hash bucket. For orderly saving of the hash keys, a hash table is used that links to a non-empty hash bucket [28].…”

Section: Locality-sensitive Hashingmentioning

confidence: 99%

Evaluation of Gist Operator for Document Image Retrieval

Alaei

Pal

et al. 2018

2018 13th IAPR International Workshop on Document Analysis Systems (DAS)

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As digitised documents normally contain a large variety of structures, a page segmentation-and layout-free method for document image retrieval is preferable. In this research work, therefore, wavelet transform as a transformbased approach is initially used to provide different undersampled images from the original image. Then, Gist operator, as a feature extraction technique, is employed to extract a set of global features from the original image as well as the sub-images obtained from the wavelet transform. Moreover, the column-wise variances of the values in each sub-image are computed and they are then concatenated to obtain another set of features. Considering each feature set, locality-sensitive hashing is employed to compute similarity distances between a query and the document images in the database. Finally, a classifier fusion technique using the mean function is taken into account to provide a document image retrieval result. The combination of these features and a clustering score fusion strategy provides higher document image retrieval accuracy. Two different databases of the document image are considered for experimentation. The results obtained from the experimental study are detailed and the results are encouraging.

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“…The LSH technique is still evolving (Liu et al 2014;Zhou et al 2016), widely used, and suitable for solving ANN, retrieval, classification and other problems (Rao & Zhu 2016;Liu et al 2015;Liao et al 2016;Kraus et al 2016).…”

Section: Basic Principles Of Lshmentioning

confidence: 99%

Matching CCD images to a stellar catalog using locality-sensitive hashing

Liu

Peng

2018

Res. Astron. Astrophys.

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The usage of a subset of observed stars in a CCD image to find their corresponding matched stars in a stellar catalog is an important issue in astronomical research. Subgraph isomorphic-based algorithms are the most widely used methods in star catalog matching. When more subgraph features are provided, the CCD images are recognized better. However, when the navigation feature database is large, the method requires more time to match the observing model. To solve this problem, this study investigates further and improves subgraph isomorphic matching algorithms. We present an algorithm based on a locality-sensitive hashing technique, which allocates quadrilateral models in the navigation feature database into different hash buckets and reduces the search range to the bucket in which the observed quadrilateral model is located. Experimental results indicate the effectivity of our method.

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A novel locality-sensitive hashing algorithm for similarity searches on large-scale hyperspectral data

Cited by 8 publications

References 8 publications

Document Image Retrieval Based on Visual Saliency Maps

Document Image Retrieval Based on Visual Saliency Maps

Evaluation of Gist Operator for Document Image Retrieval

Matching CCD images to a stellar catalog using locality-sensitive hashing

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