The Wikipedia Image Retrieval Task

Tsikrika, Theodora; Kludas, Jana

doi:10.1007/978-3-642-15181-1_9

Cited by 30 publications

(25 citation statements)

References 21 publications

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“…5 and 6. We show the applicability of our model on a multimodal domain by using the ImageCLEF 2011 Wikipedia collection dataset [47]. Finally, our study is summarized in Sect.…”

Section: Fig 1 Different Types Of Relations In the Modelmentioning

confidence: 99%

“…Related research on the ImageCLEF 2011 Wikipedia collection is generally based on a combination of text and image retrieval [47]. To our best knowledge, there is no approach that has modelled the collection as a graph structure and no approach has therefore leveraged the explicit links between information objects and between information objects and their features.…”

Section: Structured Mmirmentioning

confidence: 99%

“…query scenarios/information needs). The topics are divided into four categories of easy (17 topics), medium (10 topics), hard (16 topics) and very hard (7 topics) [47]. They cover types of queries that are supposedly better solved by textual, visual or multimodal retrieval.…”

Section: Experiments Designmentioning

confidence: 99%

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A faceted approach to reachability analysis of graph modelled collections

Sabetghadam

Lupu

Bierig

et al. 2017

Int J Multimed Info Retr

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Nowadays, there is a proliferation of available information sources from different modalities-text, images, audio, video and more. Information objects are not isolated anymore. They are frequently connected via metadata, semantic links, etc. This leads to various challenges in graph-based information retrieval. This paper is concerned with the reachability analysis of multimodal graph modelled collections. We use our framework to leverage the combination of features of different modalities through our formulation of faceted search. This study highlights the effect of different facets and link types in improving reachability of relevant information objects. The experiments are performed on the Image CLEF 2011 Wikipedia collection with about 400,000 documents and images. The results demonstrate that the combination of different facets is conductive to obtain higher reachability. We obtain 373% recall gain for very hard topics by using our graph model of the collection. Further, by adding semantic links to the collection, we gain a 10% increase in the overall recall.

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“…5 and 6. We show the applicability of our model on a multimodal domain by using the ImageCLEF 2011 Wikipedia collection dataset [47]. Finally, our study is summarized in Sect.…”

Section: Fig 1 Different Types Of Relations In the Modelmentioning

confidence: 99%

Section: Structured Mmirmentioning

confidence: 99%

See 1 more Smart Citation

A faceted approach to reachability analysis of graph modelled collections

Sabetghadam

Lupu

Bierig

et al. 2017

Int J Multimed Info Retr

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“…The proposed multimedia retrieval framework is evaluated in three datasets, namely the the WIKI11 [26], the IAPR-TC12 [10] and the MediaEval dataset from the diversity task of 2015 [13]. The WIKI11 dataset has 237,434 images with descriptions in one to three languages and 50 topics with one to five query images with caption.…”

Section: Dataset Descriptionmentioning

confidence: 99%

“…The WIKI11 and IAPR-TC12 datasets have been annotated by means of the ImageCLEF campaign [10,26]. A title and a short description correspond to each image of both datasets, thus formulating the textual modality.…”

Section: Dataset Descriptionmentioning

confidence: 99%

Multimedia retrieval based on non-linear graph-based fusion and partial least squares regression

Gialampoukidis

Moumtzidou

Liparas

et al. 2017

Multimed Tools Appl

Self Cite

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Heterogeneous sources of information, such as images, videos, text and metadata are often used to describe different or complementary views of the same multimedia object, especially in the online news domain and in large annotated image collections. The retrieval of multimedia objects, given a multimodal query, requires the combination of several sources of information in an efficient and scalable way. Towards this direction, we provide a novel unsupervised framework for multimodal fusion of visual and textual similarities, which are based on visual features, visual concepts and textual metadata, integrating non-linear graph-based fusion and Partial Least Squares Regression. The fusion strategy is based on the construction of a multimodal contextual similarity matrix and the non-linear combination of relevance scores from query-based similarity vectors. Our framework can employ more than two modalities and high-level information, without increase in memory complexity, when compared to state-of-the-art baseline methods. The experimental comparison is done in three public multimedia collections in the multimedia retrieval task. The results have shown that the proposed method outperforms the baseline methods, in terms of Mean Average Precision and Precision@20.

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