Utilizing similarity relationships among existing data for high accuracy processing of content-based image retrieval

Thanh, Hai; Kim, Myoung Ho

doi:10.1007/s11042-013-1360-9

Cited by 4 publications

(3 citation statements)

References 30 publications

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“…We also aim to map the currently available XML format into a web ontology, in order to give users the possibility to insert semantic metadata for each annotated object, which could not only support interoperability with other semantic web applications (e.g. multimedia retrieval, like in [17,25,34]), but also enable users to generate ground truth for higher level tasks (e.g. object recognition etc.…”

Section: Discussionmentioning

confidence: 99%

An innovative web-based collaborative platform for video annotation

Kavasidis

Palazzo

Salvo

et al. 2013

Multimed Tools Appl

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Large scale labeled datasets are of key importance for the development of automatic video analysis tools as they, from one hand, allow multi-class classifiers training and, from the other hand, support the algorithms' evaluation phase. This is widely recognized by the multimedia and computer vision communities, as witnessed by the growing number of available datasets; however, the research still lacks in annotation tools able to meet user needs, since a lot of human concentration is necessary to generate high quality ground truth data. Nevertheless, it is not feasible to collect large video ground truths, covering as much scenarios and object categories as possible, by exploiting only the effort of isolated research groups. In this paper we present a collaborative web-based platform for video ground truth annotation. It features an easy and intuitive user interface that allows plain video annotation and instant sharing/integration of the generated ground truths, in order to not only alleviate a large part of the effort and time needed, but also to increase the quality of the generated annotations. The tool has been on-line in the last four months and, at the current date, we have collected about 70,000 annotations. A comparative

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Section: Discussionmentioning

confidence: 99%

An innovative web-based collaborative platform for video annotation

Kavasidis

Palazzo

Salvo

et al. 2013

Multimed Tools Appl

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“…However, these approaches fail to return satisfactory results in many situations, mainly due to the well-known semantic gap problem [21][22][23]. This has motivated research attempts to improve distance metrics in CBIR systems in the past few years [2,[7][8][9][10][11][12][13]24], leading to promising results considering several approaches and post-processing techniques [25][26][27][28].…”

Section: Image Retrieval and Re-ranking In Cbir Tasksmentioning

confidence: 99%

“…The use of context can also play an important role in CBIR applications; nonetheless, traditional systems usually perform only pairwise image analysis (i.e., they compute similarity or distance measures considering only pairs of images) [4]. Because of this, many CBIR approaches [7,8,10,12,14,23,24,29] were recently proposed to improve the effectiveness of retrieval tasks by replacing the use of pairwise similarities with more global affinity metrics that consider the relationship among collection objects without needing labeled training data. Figure 2 shows an example of how the use of contextual information can improve the result of CBIR systems.…”

Section: Image Retrieval and Re-ranking In Cbir Tasksmentioning

confidence: 99%

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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Comparative assessment of efficiency for content based image retrieval systems using different wavelet features and pre-classifier

Chowdhury

Kundu

2014

Multimed Tools Appl

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Utilizing similarity relationships among existing data for high accuracy processing of content-based image retrieval

Cited by 4 publications

References 30 publications

An innovative web-based collaborative platform for video annotation

An innovative web-based collaborative platform for video annotation

A unified model for accelerating unsupervised iterative re‐ranking algorithms

Comparative assessment of efficiency for content based image retrieval systems using different wavelet features and pre-classifier

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