Information Fusion for Combining Visual and Textual Image Retrieval

Zhou, Xin; Depeursinge, Adrien; Müller, Henning

doi:10.1109/icpr.2010.393

Cited by 25 publications

(19 citation statements)

References 12 publications

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“…simple linear weighting, principle component analysis (van Zwol, 2005), or using a weighted schema for aggregating features based on item scores (Wilkins, Ferguson, & Smeaton, 2006). In Zhou, Depeursinge, and Müller (2010), traditional approaches such as maximum combination (comb-MAX), sum combination (combSUM), and others, were employed. The results show that fused runs outperform the best single-medium runs.…”

Section: Two-stage Vs Fusion Of Mediamentioning

confidence: 99%

Dynamic two-stage image retrieval from large multimedia databases

Arampatzis

Zagoris

Chatzichristofis

2013

Information Processing & Management

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Section: Two-stage Vs Fusion Of Mediamentioning

confidence: 99%

Dynamic two-stage image retrieval from large multimedia databases

Arampatzis

Zagoris

Chatzichristofis

2013

Information Processing & Management

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“…ImageCLEF 2 uses a similar approach to TREC, albeit the task is limited to image retrieval. Data from this conference has also been used for fusion [16].…”

Section: Background and Motivationmentioning

confidence: 99%

“…• Precision at n documents (P@n) [3,[5][6][7]9,11,12,14,15,17,18]. • Average Precision (AP) or Mean Average Precision (MAP) [2][3][4][5][6][7][8][9][10][11][12][13][14][15][16][17][18]. • Precision/Recall curves [4,10,12].…”

Section: A Use Of Standard Ir Metricsmentioning

confidence: 99%

“…This is intuitively reasonable, as it acknowledges that merging result sets does not guarantee an improvement in quality. In the majority of cases, the comparison is made with the component system that has achieved the highest score according to the metric being used [2,4,5,10,[13][14][15][16][17]. A fusion method that improves upon this justifies its development by indicating that it is not possible to achieve equivalent results simply by choosing a single highperforming IR system.…”

Section: B Comparison With Component Systemsmentioning

confidence: 99%

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Towards an open science platform for the evaluation of data fusion

Huang

Chen

Meng

et al. 2018

2018 IEEE 3rd International Conference on Cloud Computing and Big Data Analysis (ICCCBDA)

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Combining the results of different search engines in order to improve upon their performance has been the subject of many research papers. This has become known as the "Data Fusion" task, and has great promise in dealing with the vast quantity of unstructured textual data that is a feature of many Big Data scenarios. However, no universally-accepted evaluation methodology has emerged in the community. This makes it difficult to make meaningful comparisons between the various proposed techniques from reading the literature alone. Variations in the datasets, metrics, and baseline results have all contributed to this difficulty.This paper argues that a more unified approach is required, and that a centralised software platform should be developed to aid researchers in making comparisons between their algorithms and others. The desirable qualities of such a system have been identified and proposed, and an early prototype has been developed. Re-implementing algorithms published by other researchers is a great burden on those proposing new techniques. The prototype system has the potential to greatly reduce this burden and thus encourage more comparable results being generated and published more easily.

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“…Given the results from the text-and content-based retrievals, we conducted the fusion retrieval by using the sum combination method, which has been effective for textual and visual feature fusion [33]. To do this, a normalization step was firstly incorporated to normalize the similarity scores obtained from the aforementioned results, as:…”

Section: Fusion Retrievalmentioning

confidence: 99%

Text- and Content-Based Medical Image Retrieval in the VISCERAL Retrieval Benchmark

Zhang

Song

Cai

et al. 2017

Cloud-Based Benchmarking of Medical Image Analysis

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Text-and content-based retrieval are the most widely used approaches for medical image retrieval. They capture the similarity between the images from different perspectives: text-based methods rely on manual textual annotations or captions associated with images; content-based approaches are based on the visual content of the images themselves such as colours and textures. Text-based retrieval can better meet the high-level expectations of humans but is limited by the timeconsuming annotations. Content-based retrieval can automatically extract the visual features for high-throughput processing; however, its performance is less favourable than the text-based approaches due to the gap between low-level visual features and high-level human expectations. In this chapter, we present the participation from our joint research team of USYD/HES-SO in the VISCERAL retrieval task. Five different methods are introduced, of which two are based on the anatomy-pathology terms, two are based on the visual image content and the last one is based on the fusion of the aforementioned methods. The comparison results, given the different methods indicated that the text-based methods outperformed the content-based retrieval and the fusion of text and visual contents, generated the best performance overall.

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Information Fusion for Combining Visual and Textual Image Retrieval

Cited by 25 publications

References 12 publications

Dynamic two-stage image retrieval from large multimedia databases

Dynamic two-stage image retrieval from large multimedia databases

Towards an open science platform for the evaluation of data fusion

Text- and Content-Based Medical Image Retrieval in the VISCERAL Retrieval Benchmark

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