Image re-ranking and rank aggregation based on similarity of ranked lists

Pedronette, Daniel Carlos Guimarães; Torres, Ricardo da Silva

doi:10.1016/j.patcog.2013.01.004

Cited by 90 publications

(107 citation statements)

References 41 publications

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“…Various image re-ranking schemes such as Distance Optimization Algorithm [33], RL-Sim reranking algorithm [15] and Reciprocal kNN Graphs based manifold learning (RKNN-ML) algorithm [34] have been evaluated in comparison with the proposed scheme by considering both low-level and high-level descriptors for all the four datasets. Tables 2 and 3 summarizes the mean average precision, P@20 and average R-precision values obtained with the proposed approach for various low-level and high-level descriptors under the following circumstances: before and after the use of the proposed re-ranking scheme in image retrieval.…”

Section: Retrieval Resultsmentioning

confidence: 99%

“…However, correlation-based approaches aims to improve the retrieval effectiveness by replacing the pairwise image similarity calculation using global affinity measures which incorporate the correspondence among all the images in the database. In this regard, graph transduction [30], diffusion process [31], affinity learning [32] and context-based algorithms [15,33,34] have been introduced. Among all these approaches, context-based reranking is more prominent and it requires special attention.…”

Section: Image Re-rankingmentioning

confidence: 99%

“…In essence, DOA exploits the fact that if two images are similar their distances to the rest of the images in the dataset and the corresponding retrieval lists when these two images supplied as query should be identical. Later on, RL-Sim algorithm is introduced by Pedronette and Torres [15] for image re-ranking. It is basically an iterative approach where the distance between images is updated in each step based on the similarity of the retrieval lists of the database images.…”

Section: Image Re-rankingmentioning

confidence: 99%

“…These rank list optimization techniques can be grouped into three main categories: (i) approaches based on relevance feedback (RF) [1][2][3][4][5][6], (ii) fusion models [7][8][9] and (iii) re-ranking methods [10][11][12][13][14][15]. Relevance feedback incorporates user judgements in the retrieval process.…”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

On integrating re-ranking and rank list fusion techniques for image retrieval

Arun

Govindan

Kumar

2017

Int J Data Sci Anal

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This paper aims to unify image re-ranking and rank aggregation strategies to enhance the retrieval precision of content-based image retrieval (CBIR) systems. In general, CBIR systems are concerned with the retrieval of a set of relevant images from large repositories in response to a submitted query. The primary objective of CBIR systems is the exact ordering of database images in accordance with the presented query. To this end, we present a novel image re-ranking scheme for reordering the initial search results returned by multiple retrieval models and an efficient rank list fusion scheme to combine these refined retrieval results to achieve better performance. The re-ranking algorithm introduced in this work utilizes distance correlation coefficient to refine the search result generated by a given retrieval model. It involves two-step clustering of the initial retrieval list followed by an adaptive procedure for updating the similarity scores among images based on the created clusters. Similarly, the Particle Swarm Optimization-based similarity score fusion framework presented in this work optimally combines the retrieval results generated by multiple CBIR systems. The proposed approach is evaluated on various retrieval tasks using state-of-the-art low-level and high-level descriptors. Experimental results show that our model can significantly enhance the overall effectiveness of CBIR systems.

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Section: Retrieval Resultsmentioning

confidence: 99%

Section: Image Re-rankingmentioning

confidence: 99%

Section: Image Re-rankingmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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On integrating re-ranking and rank list fusion techniques for image retrieval

Arun

Govindan

Kumar

2017

Int J Data Sci Anal

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“…Usually, in a typical RF-based search scenario, only a few collection images are labeled. That opens a new area of investigation concerning the use of unsupervised approaches [5][6][7] that somehow can take advantage of the large number of unlabeled images available in a query session. Usually, these approaches benefit from contextual information defined in terms of information that can be extracted from the relationships among images (e.g., their distances and computed ranked lists).…”

Section: Introductionmentioning

confidence: 99%

A semi-supervised learning algorithm for relevance feedback and collaborative image retrieval

Pedronette

Calumby

Torres

2015

J Image Video Proc.

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The interaction of users with search services has been recognized as an important mechanism for expressing and handling user information needs. One traditional approach for supporting such interactive search relies on exploiting relevance feedbacks (RF) in the searching process. For large-scale multimedia collections, however, the user efforts required in RF search sessions is considerable. In this paper, we address this issue by proposing a novel semisupervised approach for implementing RF-based search services. In our approach, supervised learning is performed taking advantage of relevance labels provided by users. Later, an unsupervised learning step is performed with the objective of extracting useful information from the intrinsic dataset structure. Furthermore, our hybrid learning approach considers feedbacks of different users, in collaborative image retrieval (CIR) scenarios. In these scenarios, the relationships among the feedbacks provided by different users are exploited, further reducing the collective efforts. Conducted experiments involving shape, color, and texture datasets demonstrate the effectiveness of the proposed approach. Similar results are also observed in experiments considering multimodal image retrieval tasks.

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A unified model for accelerating unsupervised iterative re‐ranking algorithms

Pisani

Valem

Pedronette

et al. 2020

Concurrency and Computation

Self Cite

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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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Image re-ranking and rank aggregation based on similarity of ranked lists

Cited by 90 publications

References 41 publications

On integrating re-ranking and rank list fusion techniques for image retrieval

On integrating re-ranking and rank list fusion techniques for image retrieval

A semi-supervised learning algorithm for relevance feedback and collaborative image retrieval

A unified model for accelerating unsupervised iterative re‐ranking algorithms

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