Hierarchical information quadtree: efficient spatial temporal image search for multimedia stream

Zhang, Chengyuan; Chen, Ruipeng; Zhu, Lei; Liu, Yuxin; Lin, Yunwu; Huang, Fang

doi:10.1007/s11042-018-6284-y

Cited by 18 publications

(28 citation statements)

References 58 publications

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“…It is obvious that the deep learning based methods have much better performance than the traditional hand-crafted feature based methods. In our previous works [45], [46], we proposed to combine the spatial search techniques and visual feature representations to solve geo-multimedia retrieval problem. However, as far as we know there is no existing image retrieval approach that is suitable to address the reverse spatial visual query (RSVQ) problem.…”

Section: A Image Retrievalmentioning

confidence: 99%

Reverse Spatial Visual Top-$k$ Query

Zhu

Song

et al. 2020

IEEE Access

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With the wide application of mobile Internet techniques an location-based services (LBS), massive multimedia data with geo-tags has been generated and collected. In this paper, we investigate a novel type of spatial query problem, named reverse spatial visual top-k query (RSVQ k) that aims to retrieve a set of geo-images that have the query as one of the most relevant geo-images in both geographical proximity and visual similarity. Existing approaches for reverse top-k queries are not suitable to address this problem because they cannot effectively process unstructured data, such as image. To this end, firstly we propose the definition of RSVQ k problem and introduce the similarity measurement. A novel hybrid index, named VR 2-Tree is designed, which is a combination of visual representation of geo-image and R-Tree. Besides, an extension of VR 2-Tree, called CVR 2-Tree is introduced and then we discuss the calculation of lower/upper bound, and then propose the optimization technique via CVR 2-Tree for further pruning. In addition, a search algorithm named RSVQ k algorithm is developed to support the efficient RSVQ k query. Comprehensive experiments are conducted on four geo-image datasets, and the results illustrate that our approach can address the RSVQ k problem effectively and efficiently. INDEX TERMS Geo-image, reverse top-k query, spatial visual query, hybrid index.

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Section: A Image Retrievalmentioning

confidence: 99%

Reverse Spatial Visual Top-$k$ Query

Zhu

Song

et al. 2020

IEEE Access

Self Cite

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“…The global index is based on graph and manages the time, semantics and links between data objects and features of multi-modal scene. [7] designs and presents a hierarchical information quadtree for efficient spatial temporal image search for multimedia stream. It is spatio-temporal search system for images.…”

Section: Related Workmentioning

confidence: 99%

Spatio-Temporal Range Search using K-DSTH Indexing Structure

Gill*,

Meenakshi

2019

IJEAT

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A large amount of data which includes spatial and temporal information related to different fields like geography, satellite, medical or multimedia is generated and collected at an extraordinary scale. Such data is produced by satellites, mobile devices, emerging applications like social networking sites, photo sharing sites and many more. As the whole world is aware of the importance of such spatio-temporal data, a great amount of research work is evolving around efficient storage structures and algorithms to handle a variety of spatio-temporal queries. In this paper, the authors are introducing a novel spatio-temporal indexing structure k-dStH which is an extension of k-dSTHash indexing structure. It uses master hash table, local hash table and B-tree additionally which are based on timestamp values. The researchers also introduce an algorithm k-dStHSpaTempRangeSrch based on the proposed indexing structure to find spatio-temporal objects in given spatial range at particular temporal value. The performance analysis shows that the algorithm proposed by the authors is far more efficient as compared to brute force technique of searching for the spatio-temporal objects.

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“…Albanesi et al [25] proposed a novel class of image similarity metrics based Image Retrieval. Content-based image retrieval (CBIR for short) [43][44][45][46] is to retrieve images by analyzing visual contents, and therefore image representation [18,47] plays an important role in this task. In recent years, the task of CBIR has attracted more and more attentions in the multimedia [21,48,49] and computer vision community [19,20].…”

Section: Related Workmentioning

confidence: 99%

Efficient Multimedia Similarity Measurement Using Similar Elements

Long¹,

Zhu²,

Yuan³

et al. 2019

Preprint

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Online social networking techniques and large-scale multimedia retrieval are developing rapidly, which not only has brought great convenience to our daily life, but generated, collected, and stored large-scale multimedia data as well. This trend has put forward higher requirements and greater challenges on massive multimedia retrieval. In this paper, we investigate the problem of image similarity measurement, which is one of the key problems of multimedia retrieval. Firstly, the definition of similarity measurement of images and the related notions are proposed. Then, an efficient similarity measurement framework is proposed. Besides, we present a novel basic method of similarity measurement named SMIN. To improve the performance of similarity measurement, we carefully design a novel indexing structure called SMI Temp Index (SMII for short). Moreover, we establish an index of potential similar visual words off-line to solve to problem that the index cannot be reused. Experimental evaluations on two real image datasets demonstrate that the proposed approach outperforms state-of-the-arts.

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Hierarchical information quadtree: efficient spatial temporal image search for multimedia stream

Cited by 18 publications

References 58 publications

Reverse Spatial Visual Top-$k$ Query

Reverse Spatial Visual Top-$k$ Query

Spatio-Temporal Range Search using K-DSTH Indexing Structure

Efficient Multimedia Similarity Measurement Using Similar Elements

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