High-dimensional indexing technologies for large scale content-based image retrieval: a review

Ai, Liefu; Yu, Jiguo; He, Yansong; Guan, Tao

doi:10.1631/jzus.cide1304

Cited by 25 publications

(10 citation statements)

References 36 publications

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“…The goal here is to project data into another space to highlight certain structures by identifying only the interested projections. Many methods have been proposed in the literature and discussed by some comprehensive studies [19,[182][183][184]. In the following, we discuss the popular methods of dimensionality reduction and indexing in the field of CBIR with some important advances on these tasks.…”

Section: Dimensionality Reduction and Indexingmentioning

confidence: 99%

“…Many indexing techniques represent the data space of images into a tree hierarchical structure which can be categorized as tree-based methods [19]. The non-leaf nodes are directory nodes where the information of data space is stored, and the leaf nodes are data objects that store the information to be indexed.…”

Section: ) Tree-based Indexingmentioning

confidence: 99%

“…The hash-based high-dimensional indexing projects data features from high dimensions to low dimensions via those hash functions that can be broadly categorized into two groups: sensitive hashing and spectral hashing. Sensitive hashing methods such as the local sensitivity hashing (LSH) [201] project feature points into a low-dimensional Euclidian space, while spectral hashing methods [202] map the close data points in the Euclidian space to similar binary codes in a low dimensional Hamming space [19]. Some of recent works on hash-based indexing are multi-probe KLSH [203], semantic hashing [204], sparse spectral hashing (SSH) [205], and spherical hashing [206].…”

Section: ) Tree-based Indexingmentioning

confidence: 99%

See 2 more Smart Citations

Semantic content-based image retrieval: A comprehensive study

Alzu’bi

Amira

Ramzan

2015

Journal of Visual Communication and Image Representation

185

106

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Section: Dimensionality Reduction and Indexingmentioning

confidence: 99%

Section: ) Tree-based Indexingmentioning

confidence: 99%

Section: ) Tree-based Indexingmentioning

confidence: 99%

See 1 more Smart Citation

Semantic content-based image retrieval: A comprehensive study

Alzu’bi

Amira

Ramzan

2015

Journal of Visual Communication and Image Representation

185

106

View full text Add to dashboard Cite

“…Sophisticated video indexing techniques have been developed to accelerate the search speed, such as tree-based [8], [27] and hashing [15], [26], [28]. Treebased indexing partitions the video/image representation space from coarse to fine and forms a hierarchical tree structure [29]. One example work is [8] which presents a hierarchical filter-and-refine framework for video copy detection and copy segment localization.…”

Section: A Indexing Techniquesmentioning

confidence: 99%

Unsupervised t-Distributed Video Hashing and Its Deep Hashing Extension

Hao

Goulermas

et al. 2017

IEEE Trans. on Image Process.

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Abstract-In this work, a novel unsupervised hashing algorithm, referred to as t-USMVH, and its extension to unsupervised deep hashing, referred to as t-UDH, are proposed to support large-scale video-to-video retrieval. To improve robustness of the unsupervised learning, t-USMVH combines multiple types of feature representations and effectively fuses them by examining a continuous relevance score based on a Gaussian estimation over pairwise distances, and also a discrete neighbor score based on the cardinality of reciprocal neighbors. To reduce sensitivity to scale changes for mapping objects that are far apart from each other, Student t-distribution is used to estimate the similarity between the relaxed hash code vectors for keyframes. This results in more accurate preservation of the desired unsupervised similarity structure in the hash code space. By adapting the corresponding optimization objective and constructing the hash mapping function via a deep neural network, we develop a robust unsupervised training strategy for a deep hashing network. The efficiency and effectiveness of the proposed methods are evaluated on two public video collections via comparisons against multiple classical and state-of-the-art methods.

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“…A recent survey on approximate nearest neighbor queries in general has been provided in [19]. Another survey addressing highdimensional indexing especially in the context of image retrieval has been published as well [2].…”

Section: Related Workmentioning

confidence: 99%

Retrieval of Binary Features in Image Databases: A Study

Niedermayer

Kröger

2014

Similarity Search and Applications

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Abstract. Many state-of-the art object recognition systems rely on local image features, sometimes hundreds per image, that describe the surroundings of detected interest points by a high-dimensional feature vector. To recognize objects, these systems have to match features detected in a query image against the features stored in a database containing millions or even billions of feature vectors. Hence, efficient matching is crucial for real applications. In the past, feature vectors were often real-valued, and therefore research focused on such feature representations. Present techniques, however, involve binary features to reduce memory consumption and to speed up the feature extraction stage. Matching such binary features received relatively little attention in the computer vision community. Often, either Locality Sensitive Hashing (LSH) or quantization-based techniques, that are known from real-valued features, are used. However, an indepth evaluation of the involved parameters in binary space has, to the best of our knowledge, not yet been performed. In this paper, we aim at closing this research gap, providing valuable insights for application-oriented research.

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High-dimensional indexing technologies for large scale content-based image retrieval: a review

Cited by 25 publications

References 36 publications

Semantic content-based image retrieval: A comprehensive study

Semantic content-based image retrieval: A comprehensive study

Unsupervised t-Distributed Video Hashing and Its Deep Hashing Extension

Retrieval of Binary Features in Image Databases: A Study

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