Exploring multimedia in a keyword space

Magalhães, João; Ciravegna, Fabio; Rüger, Stefan

doi:10.1145/1459359.1459374

Cited by 10 publications

(6 citation statements)

References 31 publications

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“…However, since manual annotation is a tedious and difficult task, there have been many attempts to automate it. Some researchers claim that retrievals involving automatically generated annotations are sometimes as accurate as those involving manual annotations [18].…”

Section: Textual Descriptorsmentioning

confidence: 99%

Image retrieval: Research and use in the information explosion

Inoue

2009

Prog. Inform.

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We surveyed the current research on the retrieval and utilization of images. This paper provides an overview of the difficulties and possibilities of technological or technology-related research topics resulting from the huge amount of images currently available and highlights some of the important research topics. We looked at the ongoing research activities and analyzed them from four aspects, information access and organization technology, the computing infrastructure that enables access to large-scale image resources, issues in human-system interaction and human factors related to using images, and the social aspect of image media. On the technical side, we noticed that as the number of digital images increases, so does the importance of the accuracy and scalability in relation to the image retrieval. The accuracy and scalability are in fact needed to cope with the current explosion in digital images. On the social side, not so long ago, image retrieval technologies were only experimental tools or used by experts within a limited domain. However, now the general public has access to a wide range of digital images, which means that image retrieval technologies are being used by various users in a large diversity of social contexts. Thus, as we show in this paper, the accuracy and scalability are not the only important factors in the era of information explosion, but that researchers must also be concerned with the social aspects of these technologies.

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Section: Textual Descriptorsmentioning

confidence: 99%

Image retrieval: Research and use in the information explosion

Inoue

2009

Prog. Inform.

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“…The wide acceptance of Web 2.0 has resulted in a humongous amount of resources in folksonomy systems such as Flickr 1 , Delicious 2 and Youtube 3 . For example, as of October 2009, Flickr hosted more than 4 billion images with manual tags from its users.…”

Section: Introductionmentioning

confidence: 99%

“…The problem of building connections between images and text has been well-studied [1][2] [3] [4] in the past. However, these methods always incur high computation overhead and involve complex learning algorithms.…”

Section: Introductionmentioning

confidence: 99%

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Cross Domain Search by Exploiting Wikipedia

Liu

Jiang

et al. 2012

2012 IEEE 28th International Conference on Data Engineering

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Abstract-The abundance of Web 2.0 resources in various media formats calls for better resource integration to enrich user experience. This naturally leads to a new cross domain resource search requirement, in which a query is a resource in one modal and the results are closely related resources in other modalities. With cross domain search, we can better exploit existing resources.Intuitively, tags associated with Web 2.0 resources are a straightforward medium to link resources with different modality together. However, tagging is by nature an ad hoc activity. They often contain noises and are affected by the subjective inclination of the tagger. Consequently, linking resources simply by tags will not be reliable. In this paper, we propose an approach for linking tagged resources to concepts extracted from Wikipedia, which has become a fairly reliable reference over the last few years. Compared to the tags, the concepts are therefore of higher quality. We develop effective methods for cross-modal search based on the concepts associated with resources. Extensive experiments were conducted, and the results show that our solution achieves good performance.

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“…Rather than comparing a query image using complex low-level feature descriptors it is possible to use the "keyword space" to index complex media objects using textual semantic labels and hence improve the performance and user experience [3]. In this way, even less accurate semantic labels are still incredibly useful as a single descriptor classifying visually similar images.…”

Section: Introductionmentioning

confidence: 99%

Conservation of effort in feature selection for image annotation

Little

Rüger

2009

2009 IEEE International Workshop on Multimedia Signal Processing

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How to cite:Little, Suzanne and Rüger, Stefan (2009 Abstract-This paper describes an evaluation of a number of subsets of features for the purpose of image annotation using a non-parametric density estimation algorithm (described in [1]). By applying some general recommendations from the literature and through evaluating a range of low-level visual feature configurations and subsets, we achieve an improvement in performance, measured by the mean average precision, from 0.2861 to 0.3800. We demonstrate the significant impact that the choice of visual or low-level features can have on an automatic image annotation system. There is often a large set of possible features that may be used and a corresponding large number of variables that can be configured or tuned for each feature in addition to other options for the annotation approach. Judicious and effective selection of features for image annotation is required to achieve the best performance with the least user design effort. We discuss the performance of the chosen feature subsets in comparison with previous results and propose some general recommendations observed from the work so far.

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Exploring multimedia in a keyword space

Cited by 10 publications

References 31 publications

Image retrieval: Research and use in the information explosion

Image retrieval: Research and use in the information explosion

Cross Domain Search by Exploiting Wikipedia

Conservation of effort in feature selection for image annotation

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