Quadtree representations for storage and manipulation of clusters of images

Manouvrier, Maude; Rukoz, Marta; Jomier, Geneviève

doi:10.1016/s0262-8856(02)00027-6

Cited by 16 publications

(5 citation statements)

References 35 publications

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“…By default the value of the coefficient W k (the weight of the node k) is W k =4 -P , where p is the level of the node K in the Quad-tree as done by many methods in the literature that deal with quad-tree structures (Manouvrier et al, 2002):…”

Section: Retrieval Of Similar Imagesmentioning

confidence: 99%

Shape Codification Indexing and Retrieval Using the Quad-Tree Structure

Aouat

2013

International Journal of Computer Vision and Image Processing

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The author presents in this paper a new approach for indexing and Content-based image retrieval based on the Quad-tree structure. The 3D objects are represented by their silhouettes and codified following the filling rate of each quadrant at different levels of the quad-tree subdivision. The author proposes a modified linear codification for silhouettes, this method improves the processing time because, in opposite to the traditional algorithms, the author’s algorithm has not a processing time that is proportional to the number of pixels in the image. As the same descriptor may characterize a set of different shapes, the author proposes also, efficient similarity measures to distinguish different objects having the same index in order to apply the approach to the retrieval process.

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Section: Retrieval Of Similar Imagesmentioning

confidence: 99%

Shape Codification Indexing and Retrieval Using the Quad-Tree Structure

Aouat

2013

International Journal of Computer Vision and Image Processing

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“…Oracle GeoRaster [43]allows storing the bounding boxes and derived attributes of tile images as vector geospatial data, which subsequently can be indexed and queried so that only selected tile images need to be retrieved for display. A few of existing works have addressed the issue of managing a set of similar/related rasters based on overlapping quadtrees [62][34] [35]. The techniques are similar to indexing spatial-temporal vector geospatial data such as Historical R-Tree [38], MV3R-Tree [59], TPR-Tree [51] from a methodology perspective.…”

Section: Indexing Raster Geospatial Datamentioning

confidence: 99%

Indexing large-scale raster geospatial data using massively parallel GPGPU computing

Zhang

You

Gruenwald

2010

Proceedings of the 18th SIGSPATIAL International Conference on Advances in Geographic Information Systems

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Advances in geospatial technologies have generated large amounts of raster geospatial data. Massively parallel General Purpose Graphics Processing Unit (GPGPU) computing technologies have provided personal computers with tremendous computing capabilities. In this paper, we report our work on fast indexing of large-scale raster geospatial data using GPGPU computing. We have designed a cache conscious, pointerless quadtree data structure (CCQ-Tree) that has low memory footprint, is suitable for GPU indexing and allows fast mapping between main memory and hard drives. A set of algorithms have been developed and integrated to construct CCQ-Trees on GPU devices by utilizing multiple pyramid data structures and Z-order based prefix sum. Experiments on multiple 4096*4096 blocks of a global precipitation raster data have shown that CCQ-Tree indexing using an Nvidia Quadro FX3700 GPU device reduces construction times from around 9.83 seconds to 0.42 seconds (23X speedup). The experiment results have led us to project the possibility of real time indexing of global 30 arc-seconds (approximately 1km) resolution data (43200*21600) in 1-5 seconds on a personal workstation with 1-4 Nvidia next generation Fermi GPU devices.

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“…However, using the approximate values of internal nodes appearing at level 2, with δ = L 1 : S (2) (1, 2) = |1/4 − 1/2| * 1/9 = 1/36. Several approaches, presented and compared in Manouvrier et al (2002), propose to store similar images organized in quadtrees. Their main goal is to reduce the memory space used by image quadtrees by sharing common parts between quadtrees.…”

Section: S-distancementioning

confidence: 99%

“…As a consequence, the S-distance can be used in these approaches to organize the images in a hierarchy: an image i is a descendant of an image i , in the hierarchy, if for all image j of the hierarchy: S(i, i ) ≤ S(i, j). The smaller the S-distance between image trees is, the greater the sharing of node values between their trees is-see in Manouvrier et al (2002) for more details. However, two images i and j with two very different multi-level feature vectors, i.e.…”

Section: S-distancementioning

confidence: 99%

“…In this case, δ computes the difference between the proportion of feature points stored in each node n. Each weight w n corresponds to the proportion of feature points in the sub-quadtree rooted by n in comparison with the total number of feature points in the entire quadtree. In Jomier et al (2000) and Manouvrier et al (2002), the S-distance is used to measure the sharing ratio between quadtrees in order to optimize the storage of similar image clusters. Lu et al (1994), Lin et al (2001) and Jomier et al (2005) represent each image of the database by a full fixed-depth balanced quadtree.…”

Section: Related Workmentioning

confidence: 99%

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Δ-distance: A family of dissimilarity metrics between images represented by multi-level feature vectors

2006

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This article presents the -distance, a family of distances between images recursively decomposed into segments and represented by multi-level feature vectors. Such a structure is a quad, a quin or a nona-tree resulting from a fixed and arbitrary image partition or from an image segmentation process. It handles positional information of image features (e.g. color, texture or shape). -distance is the generalized form of dissimilarity measures between multi-level feature vectors. Using different weights on tree nodes and different distances between nodes, distances between trees or visual similarity between images can be computed based on the general definition of . In this article, we present three -based distance families: two families of distances between tree structures, called T -distance (T for Tree) and S-distance (S for Segment), and a family of visual distances between images, called V-distance (V for Visual). The V-distance visually compares two images using their tree representation and the other two distances compare the tree structures resulting from image segmentation. Moreover, we show how existing distances between multi-level feature vectors appear to be particular cases of the -distance.Keywords Image database . Distance between quad/quin or nona-trees . Similarity of images . Similarity of image segments . Content-based image retrieval

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Quadtree representations for storage and manipulation of clusters of images

Cited by 16 publications

References 35 publications

Shape Codification Indexing and Retrieval Using the Quad-Tree Structure

Shape Codification Indexing and Retrieval Using the Quad-Tree Structure

Indexing large-scale raster geospatial data using massively parallel GPGPU computing

Δ-distance: A family of dissimilarity metrics between images represented by multi-level feature vectors

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