Relevancy tag ranking

Agrawal, Garima; Chaudhary, Rashmi; Singh, Pradeep Kumar

doi:10.1109/iccct.2011.6075169

Cited by 8 publications

(5 citation statements)

References 7 publications

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“…Agrawal and Chaudhary [1] proposed a relevance tag ranking algorithm to rank tag automatically based on their relevance with image content. Li et al [2] presented a tag relevance fusion method to solve limitations of a single measurement of tag relevance.…”

Section: Related Workmentioning

confidence: 99%

Review on Tag based Image Search by Social Re-Ranking

Mathew¹

2017

IJRASET

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There are many photos sharing websites like Flickr which allow users to annotate pictures with descriptive keywords called tags. Tag based image search helps to find images contributed by users in such social media sharing websites, which significantly support to the development of the web image retrieval and organization. How to make top positioned ranked result relevant is the challenging problem. For that we re-rank images according to visual data, semantic data and social clues. The inter-user re-ranking and intra-user re-ranking methods are used to achieve a good trade-off between the diversity and relevance performance. First we sort the images contributed by different users using inter-user ranking. Then we apply intra-user ranking method on ranked image set. Here, the most relevant image of each user is chosen from the image set. And these images form the final result. We construct an inverted index structure for image dataset to improve the searching process. Keywords: image Search; relevance; re-ranking; social media; tag based image retrieval I. INTRODUCTIONSocial media has taken the world by storm through dozens of websites and other forms of technologies improving the way people communicate with each other. There are many social media sharing websites that have millions of members allowing them to make connections, share images, search and more on a regular basis. Sites like Flickr allow users to create and share media information as well as describe the created content with tags. Figure 1 shows an example of a social image associated with user providing tags. Tags play as a major character in searching of images. Tag related image search is an effective method than content based image search. It provides a development in web image retrieval. However tags contributed by users may incomplete or ambiguous. But this is not unexpected because of the uncontrolled influence of tagging, diversity of knowledge and cultural background of the users. Applying the text based retrieval method may form unsatisfied results. Therefore, a ranking method that can explore tags and the images may provide a better social image search result. The ranking issues in tag based image retrieval have gained wide alertness among researchers in recent years. However, the ranking innovations face following difficulties in its development. First issue is tag mismatch. In social tagging all users have to label their uploaded images with their own tag in social network and share with others. Every user has their own habit to tag photos. Therefore, even for same image there will be several different tags. Thus many irrelevant tags are introduced with uploaded image .Second one is query ambiguity. Users cannot describe perfectly with their single keywords, so add little information to a user's contribution. In addition, equivalent words and polysemy are alternate reasons for the query uncertainty. Images taken at the same interval and fixed spot are fairly similar. How to tackle these issues in re-ranking is the fundamental ...

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Section: Related Workmentioning

confidence: 99%

Review on Tag based Image Search by Social Re-Ranking

Mathew¹

2017

IJRASET

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“…supported these initial efforts,Lee and Neve [26] described information about the a variant of the popular baseline neighbor voting algorithm for image retrieval process Agrawal and Chaudhary [24] described information about tag ranking algorithm as per image content. Zhu et al [25] described information about the adaptive teleportation random walk model for voting graph its design based on relationships between image tags.…”

Section: Introductionmentioning

confidence: 99%

Efficient Approach for Image Re Ranking with Diversity

Sairavali¹

2019

IJRASET

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Social media is one of the popular networks in the world. Its provides facilities creating and sharing information among various web users. Users also search images based on their requirement with the help of free tags. Tag-based image search is one of the methods of web image retrieval to get accurate image shared by web users in social networks. In this paper we describe the information about the issue of how to get Top ranked solution elements with similar and diversity. To solve the diversity issue in this research paper we implemented novel technique for Image search with diversity for image retrieval based on tags in social media. In that initially we found similarity tags among each tag then it will help to design a tag graph. In that Community detection method can be used to find semantic tags in cluster images then after inter-community and intra community ranking are applied for end results.

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“…The authors of [17] discuss a data-driven approach for ranking the tags assigned to an image, taking into account the size of the objects shown in the image. In order to determine the size of the objects shown, image segmenta-…”

Section: Related Workmentioning

confidence: 99%

“…The authors of [18] present a tag ranking method that combines a visual attention model with multi-instance learning, following a three-step procedure: 1) use of multi-instance learning to propagate global image tags to local image regions; 2) use of visual attention modeling to estimate the importance of the different image regions; and 3) ranking of the tags according to the saliency values of the corresponding image regions. Both [17] and [18] make use of image segmentation, a process that is still highly inaccurate. In addition, [18] needs a saliency map, which adds to the computational complexity.…”

Section: An Image Folksonomymentioning

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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Relevancy tag ranking

Cited by 8 publications

References 7 publications

Review on Tag based Image Search by Social Re-Ranking

Review on Tag based Image Search by Social Re-Ranking

Efficient Approach for Image Re Ranking with Diversity

Visually weighted neighbor voting for image tag relevance learning

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