Crowdsourcing annotation: Modelling keywords using low level features

Θεοδοσίου, Ζήνωνας; Tsapatsoulis, Nicolas

doi:10.1109/imsaa.2011.6156351

Cited by 9 publications

(5 citation statements)

References 10 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Several investigators tested and checked the authenticity of the crowd-sourced image annotation. Mitry et al [25] contrasted the quality with that in experts of crowdsourced picture rating. They used 100 photographs of the retinal fundus, chosen by two researchers.…”

Section: Existing Workmentioning

confidence: 99%

Hyperlink Induced Topic Search Model Together with Automatic Feature Review for Smartphone Applications

Vaseekaran¹,

Srinivasan²

2021

JUSST

View full text Add to dashboard Cite

The demand for smartphone apps has grown with the rising interest in artificial intelligence. Thanks to a vast number of applicant service applications, choosing the smartphone apps you want to use has been very complex for consumers. It is therefore essential that the customer interface is improved and that individual suggestions are made. Conventional recommendation approaches can in some cases be effective but have some drawbacks, which generally lead to unreliable recommendations. This study provides a basis for recommending smartphone applications, which is built on the algorithm of Hyperlink Induced Topic Search (HITS) in conjunction with association rule mining in this context. The approach combines the scores of authority and hub into the applications by means of downloads and ratings and not only takes into account the role of smartphone apps in alliance rules but also the trustworthiness aspect of consumers. Studies with industry data sets from the Samsung framework reveal that the proposed approach increases the recommendation precision greatly relative to conventional approaches.

show abstract

Section: Existing Workmentioning

confidence: 99%

Hyperlink Induced Topic Search Model Together with Automatic Feature Review for Smartphone Applications

Vaseekaran¹,

Srinivasan²

2021

JUSST

View full text Add to dashboard Cite

show abstract

“…This approach can easily address the problems in a cheap and quick way and helps task creators to exploit different opinions [34]. Implicit crowdsourced annotations can be easily gathered without burdening the involved users [64]. Since the collection is performed without supervision, it may contains several errors due to erroneous participant's feedback which is independent of whether or not they will receive a reward for their participation [10].…”

Section: Related Workmentioning

confidence: 99%

Image annotation: the effects of content, lexicon and annotation method

Θεοδοσίου

Tsapatsoulis

2020

Int J Multimed Info Retr

Self Cite

View full text Add to dashboard Cite

Image annotation is the process of assigning metadata to images, allowing effective retrieval by text-based search techniques. Despite the lots of efforts in automatic multimedia analysis, automatic semantic annotation of multimedia is still inefficient due to the problems in modelling high level semantic terms. In this paper we examine the factors affecting the quality of annotations collected through crowdsourcing platforms. An image dataset was manually annotated utilizing: (i) a vocabulary consists of pre-selected set of keywords, (ii) an hierarchical vocabulary, and (iii) free keywords. The results show that the annotation quality is affected by the image content itself and the used lexicon. As we expected while annotation using the hierarchical vocabulary is more representative, the use of free keywords leads to increased invalid annotation. Finally it is shown that images requiring annotations that are not directly related to their content (i.e. annotation using abstract concepts), lead to accrue annotator inconsistency revealing in that way the difficulty in annotating such kind of images is not limited to automatic annotation, but it is generic problem of annotation.

show abstract

“…al. [17] used both vocabulary keywords and free keywords to check whether guided annotation (as assumed by the use of structured vocabulary) would increase annotation consistency. The researchers concluded that, indeed, by combing free keywords and vocabulary keywords annotation consistency increases compared to the use of free keywords alone.…”

Section: Related Workmentioning

confidence: 99%

“…Visual models are then fed with image features extracted from unseen images to predict their tagging [16]. Assuming that good visual models can be achieved, image retrieval using the training by example paradigm provides a promising alternative to text-based methods (since it does not require explicit annotation of all images in the collection, but only a small set of properly annotated images) [17]. Nevertheless, the first important step to create effective visual models is to use good training examples (pairs of images and annotations).…”

Section: Introductionmentioning

confidence: 99%

Instagram Hashtags as Image Annotation Metadata

Giannoulakis

Tsapatsoulis

2015

IFIP Advances in Information and Communication Technology

Self Cite

View full text Add to dashboard Cite

Abstract. Image tagging is an essential step for developing automatic image annotation methods that are based on the learning by example paradigm. However, manual image annotation, even for creating training sets for machine learning algorithms, requires hard effort and contains human judgment errors and subjectivity. Thus, alternative ways for automatically creating training examples, i.e., pairs of images and tags, are pursued. In this work we investigate whether tags accompanying photos in social media and especially the Instagram hashtags, provide a form of image annotation. If such a claim is proved then Instagram could be a very rich source of training data, easily collectable automatically, for the development of automatic image annotation techniques. Our hypothesis is that Instagram hashtags, and especially those provided by the photo owner / creator, express more accurately the content of a photo compared to the tags assigned to a photo during explicit image annotation processes like crowdsourcing. In this context, we explore the descriptive power of hashtags by examining whether other users would use the same, with the owner, hashtags to annotate an image. For this purpose a set of 30 randomly chosen, from Instagram, images were used as a dataset for our research. Then, one to four hashtags, considered as the most descriptive ones for the image in question, were selected among the hashtags used by the image owner. Three online questionnaires with ten images each were distributed to experiment participants in order to choose the best suitable hashtag for every image according to their interpretation. Results show that an average of 55% of the participants hashtag choices coincide with those suggested by the photo owners; thus, an initial evidence towards our hypothesis confirmation can be claimed.

show abstract

Crowdsourcing annotation: Modelling keywords using low level features

Cited by 9 publications

References 10 publications

Hyperlink Induced Topic Search Model Together with Automatic Feature Review for Smartphone Applications

Hyperlink Induced Topic Search Model Together with Automatic Feature Review for Smartphone Applications

Image annotation: the effects of content, lexicon and annotation method

Instagram Hashtags as Image Annotation Metadata

Contact Info

Product

Resources

About