Image annotations by combining multiple evidence &amp; wordNet

Jin, Yohan; Khan, Latifur; Wang, Lei; Awad, Mamoun

doi:10.1145/1101149.1101305

Cited by 214 publications

(134 citation statements)

References 17 publications

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“…The authors of [10] make use of WordNet in order to measure the semantic correlation among tags assigned to a seed image. Strongly correlated tags are considered to be relevant to the content of the seed image, whereas weakly correlated tags are considered to be non-relevant.…”

Section: Related Workmentioning

confidence: 99%

Visually weighted neighbor voting for image tag relevance learning

Lee

Neve

2013

Multimed Tools Appl

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The presence of non-relevant tags in image folksonomies hampers the effective organization and retrieval of user-contributed images. In this paper, we propose to learn the relevance of user-supplied tags by means of visually weighted neighbor voting, a variant of the popular baseline neighbor voting algorithm proposed by Xirong Li et al. in 2009. To gain insight into the effectiveness of baseline and visually weighted neighbor voting, we qualitatively analyze the difference in tag relevance when using a different number of neighbors, for both tags relevant and tags not relevant to the content of a seed image. Our qualitative analysis shows that tag relevance values computed by means of visually weighted neighbor voting are more stable and representative than tag relevance values computed by means of baseline neighbor voting. This is quantitatively confirmed through extensive experimentation with MIRFLICKR-25000, studying the variation of tag relevance values as a function of the number of neighbors used (for both tags relevant and tags not relevant with respect to the content of a seed image), as well as the influence of tag relevance learning on the effectiveness of image tag refinement, tag-based image retrieval, and image tag recommendation.

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Section: Related Workmentioning

confidence: 99%

Visually weighted neighbor voting for image tag relevance learning

Lee

Neve

2013

Multimed Tools Appl

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“…We can mention here Bayes Point Machine (Chang et al 2003), Support Vector Machine (Cusano et al 2004) and Decision Trees (Kwasnicka and Paradowski 2008) which all estimate the visual features distributions associated with each word. Some authors try to refine the annotation results by reducing the difference between the expected and resulting word count vectors (Kwasnicka and Paradowski 2006), by using Word-Net which contains semantic relations between words (Jin et al 2005) or by word co-occurrence models coupled with fast random walks (Llorente et al 2009), an interesting approach exploiting the recent advances in graph processing.…”

Section: Related Approachesmentioning

confidence: 99%

Image auto-annotation with automatic selection of the annotation length

2012

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Developing a satisfactory and effective method for auto-annotating images that works under general conditions is a challenging task. The advantages of such a system would be manifold: it can be used to annotate existing, large databases of images, rendering them accessible to text search engines; or it can be used as core for image retrieval based on a query image's visual content. Manual annotation of images is a difficult, tedious and time consuming task. Furthermore, manual annotations tend to show great inter-person variance: considering an image, the opinions about what elements are significant and deserve an annotation vary strongly. The latter poses a problem for the evaluation of an automatic method, as an annotation's correctness is greatly subjective. In this paper we present an automatic method for annotating images, which addresses one of the existing methods' major limitation, namely a fixed annotation length. The proposed method, PATSI, automatically chooses the resulting annotation's length for each query image. It is held as simple as possible and a build-in parameter optimization procedure renders PATSI de-facto parameter free. Finally, PATSI is evaluated on standard datasets, outperforming various state-of-the-art methods.

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“…This paradigm, that has been proficiently used for the automatic annotation of images [4,22], is exploited in SHIATSU for automatically suggesting to the user labels at the shot level. For this, the Annotation Processor (see Fig.…”

Section: Similarity-based Labelingmentioning

confidence: 99%

SHIATSU: tagging and retrieving videos without worries

Bartolini

Patella

Romani

2011

Multimed Tools Appl

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The dramatic growth of video content over modern media channels (such as the Internet and mobile phone platforms) directs the interest of media broadcasters towards the topics of video retrieval and content browsing. Several video retrieval systems benefit from the use of semantic indexing based on content, since it allows an intuitive categorization of videos. However, indexing is usually performed through manual annotation, thus introducing potential problems such as ambiguity, lack of information, and non-relevance of index terms. In this paper, we present SHIATSU, a complete system for video retrieval which is based on the (semi-)automatic hierarchical semantic annotation of videos exploiting the analysis of visual content; videos can then be searched by means of attached tags and/or visual features. We experimentally evaluate the performance of SHIATSU on two different real video benchmarks, proving its accuracy and efficiency.

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Image annotations by combining multiple evidence & wordNet

Cited by 214 publications

References 17 publications

Visually weighted neighbor voting for image tag relevance learning

Visually weighted neighbor voting for image tag relevance learning

Image auto-annotation with automatic selection of the annotation length

SHIATSU: tagging and retrieving videos without worries

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