Locally constrained diffusion process on locally densified distance spaces with applications to shape retrieval

Yang, Xing‐Wei; Köknar-Tezel, Suzan; Latecki, Longin Jan

doi:10.1109/cvpr.2009.5206844

Cited by 155 publications

(174 citation statements)

References 20 publications

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“…After several iterations updating, P KK can describe the manifold structure of the shapes well and it is used as the final distances for retrieval. LCDP has been proved to be able to learn the sub-manifold structure of shapes very well and obtain excellent retrieval results [41]. With the help of LCDP, bull's eye score on MPEG-7 data set can reach the highest ever 95.24%, which is discussed in details below.…”

Section: Beyond Pairwise Shape Similaritymentioning

confidence: 96%

“…Compared to Fig. 6(b), which is taken from [41], none of the class bull's eye score has obvious drop after LCDP. This demonstrates the advantage of ASC compared to IDSC.…”

Section: Shape Classification Using Mpeg7 Datasetmentioning

confidence: 99%

“…Recent work clearly demonstrated that adding context information to direct pairwise shape similarity can substantially improver shape retrieval [40,15,41]. Under context of a given shape we understand here its first K nearest neighbors.…”

Section: Beyond Pairwise Shape Similaritymentioning

confidence: 99%

“…Under context of a given shape we understand here its first K nearest neighbors. However, these methods [40,15,41] mainly focus on improving the transduction algorithms. We demonstrate that a 'better' original distance matrix is also very crucial for the shape retrieval with context information.…”

Section: Beyond Pairwise Shape Similaritymentioning

confidence: 99%

“…Otherwise, totally different objects in the top retrieval results may ruin the graph structure constructed by the shapes, which makes negative impact for the graph transduction or diffusion processes to learn the shape manifold structure. In order to show that ASC is suitable for context information based shape retrieval, we use the Locally Constrained Diffusion Process (LCDP) introduced in [41] to learn the sub-manifold structure for shapes.…”

Section: Beyond Pairwise Shape Similaritymentioning

confidence: 99%

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Balancing Deformability and Discriminability for Shape Matching

Ling

Yang

Latecki

2010

Lecture Notes in Computer Science

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Abstract. We propose a novel framework, aspect space, to balance deformability and discriminability, which are often two competing factors in shape and image representations. In this framework, an object is embedded as a surface in a higher dimensional space with a parameter named aspect weight, which controls the importance of intensity in the embedding. We show that this framework naturally unifies existing important shape and image representations by adjusting the aspect weight and the embedding. More importantly, we find that the aspect weight implicitly controls the degree to which a representation handles deformation. Based on this idea, we present the aspect shape context, which extends shape context-based descriptors and adaptively selects the "best" aspect weight for shape comparison. Another observation we have is the proposed descriptor nicely fits context-sensitive shape retrieval. The proposed methods are evaluated on two public datasets, MPEG7-CE-Shape-1 and Tari 1000, in comparison to state-of-the-art solutions. In the standard shape retrieval experiment using the MPEG7 CE-Shape-1 database, the new descriptor with context information achieves a bull's eye score of 95.96%, which surpassed all previous results. In the Tari 1000 dataset, our methods significantly outperform previous tested methods as well.

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Section: Beyond Pairwise Shape Similaritymentioning

confidence: 96%

“…Compared to Fig. 6(b), which is taken from [41], none of the class bull's eye score has obvious drop after LCDP. This demonstrates the advantage of ASC compared to IDSC.…”

Section: Shape Classification Using Mpeg7 Datasetmentioning

confidence: 99%

Section: Beyond Pairwise Shape Similaritymentioning

confidence: 99%

Section: Beyond Pairwise Shape Similaritymentioning

confidence: 99%

Section: Beyond Pairwise Shape Similaritymentioning

confidence: 99%

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Balancing Deformability and Discriminability for Shape Matching

Ling

Yang

Latecki

2010

Lecture Notes in Computer Science

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Contextual Spaces Re‐Ranking: accelerating the Re‐sort Ranked Lists step on heterogeneous systems

Pisani

Pedronette

Torres

et al. 2016

Concurrency and Computation

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Re-ranking algorithms have been proposed to improve the effectiveness of content-based image retrieval systems by exploiting contextual information encoded in distance measures and ranked lists. In this paper, we show how we improved the efficiency of one of these algorithms, called Contextual Spaces Re-Ranking (CSRR). One of our approaches consists in parallelizing the algorithm with OpenCL to use the central and graphics processing units of an accelerated processing unit. The other is to modify the algorithm to a version that, when compared with the original CSRR, not only reduces the total running time of our implementations by a median of 1:6 but also increases the accuracy score in most of our test cases. Combining both parallelization and algorithm modification results in a median speedup of 5:4 from the original serial CSRR to the parallelized modified version. Different implementations for CSRR's Re-sort Ranked Lists step were explored as well, providing insights into graphics processing unit sorting, the performance impact of image descriptors, and the trade-offs between effectiveness and efficiency. vectors, which are later used for similarity assessment among images. A CBIR system ranks the collection images by decreasing order of similarity, and because users consider mostly top-ranked images, it is imperative that the rank be as accurate as possible.In recent years, several successful attempts to increase the effectiveness (quality of results) of CBIR systems have been performed [1][2][3][4][5][6]. In particular, re-ranking methods have been used to improve the effectiveness of CBIR systems by exploiting contextual information encoded in similarity scores and ranked lists. These methods are, on the other hand, very costly as they are based on comparing collection images multiple times. In a real-world scenario, CBIR systems require both good effectiveness and efficiency (response time), so re-ranking methods must be improved.Central processing units (CPUs) no longer have just one core, and graphics processing units (GPUs) are now being used as general purpose processors due to having evolved into massive parallel architectures capable of executing hundreds of operations per cycle [7]. These devices have been successfully used to accelerate re-ranking [7, 8] and retrieval [9] systems, obtaining good speedups.Therefore, alternatives that increase performance with parallelization seem to be a possible fit for the Contextual Spaces Re-Ranking (CSRR) algorithm [2], which we discuss in this paper. Another possible approach consists in analyzing the compromises between accuracy and performance that come out of modifying existing algorithms, because this can lead to eliminating demanding work.Our solution first exploits the use of parallelization to speed up the more costly steps of the CSRR algorithm, obtaining speedups of up to 3:3 for the Compute Distances step and 5:1 for the Re-sort Ranked Lists step on an accelerated processing unit (APU). We then propose a modification to this algorithm, which by itse...

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A unified model for accelerating unsupervised iterative re‐ranking algorithms

Pisani

Valem

Pedronette

et al. 2020

Concurrency and Computation

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Re-ranking algorithms are used to improve the effectiveness of multimedia retrieval systems. However, they are usually very computationally costly, and therefore demand the specification and implementation of efficient and effective big multimedia analysis approaches.Recently proposed unsupervised iterative re-ranking methods present good accuracy and significant potential for parallelization, leading us to explore efficiency vs. effectiveness trade-offs. In this paper, we introduce a class of unsupervised iterative re-ranking algorithms and present a model that can be used to guide their implementation for parallel architectures. We also analyze the impact of the parallelization on the performance of three algorithms that belong to the proposed class: Contextual Spaces, RL-Sim, and Contextual Re-Ranking. The experiments show speedups that reach up to 6.0× for Contextual Spaces Re-Ranking, 16.1× for RL-Sim Re-Ranking, and 3.3× for Contextual Re-Ranking. These results demonstrate that the proposed parallel programming model can be successfully applied to improve the scalability of multimedia retrieval systems.

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Locally constrained diffusion process on locally densified distance spaces with applications to shape retrieval

Abstract: Abstract

Cited by 155 publications

References 20 publications

Balancing Deformability and Discriminability for Shape Matching

Balancing Deformability and Discriminability for Shape Matching

Contextual Spaces Re‐Ranking: accelerating the Re‐sort Ranked Lists step on heterogeneous systems

A unified model for accelerating unsupervised iterative re‐ranking algorithms

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