Adaptive diversification for tag-based social image retrieval

Ksibi, Amel; Ammar, Anis Ben; Amar, Chokri Ben

doi:10.1007/s13735-013-0045-5

Cited by 16 publications

(5 citation statements)

References 24 publications

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“…On the other hand, paper proposes a new ranking process method called adaptive diverse relevance ranking which automatically forecast an effective trade-off between relevance scores and diversity scores according to the query ambiguity level. with experiments using 12 ambiguous queries over the (NUS-WIDE) data-set show the effectiveness of our approach versus classical uniform diversification approaches [3].…”

Section: Liturature Surveymentioning

confidence: 87%

A Survey on Tag Based Image Search by Social Re-ranking

2017

IJRTER

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Abstract-Day to day the amount of tablet or mobile devices and web services are increasing speedily and the victory of many online social media websites, which allow users to create and share media information such as images and videos as well as describe the created content with tags. Social media sharing websites like "Flickr" or "Picasa" allow users to annotate or tag images with free tags, which notably support to the development of the web image retrieval and organization. Tag-based image search is an important and effective method than Content based image search to find images contributed by social users in such social websites like Flickr and Picasa. However, how to make the top ranked image result relevant and with diversity is challenging. The paper propose a social re-ranking system for tag-based image retrieval with the consideration of image's relevance and diversity [1]. Here, the focus at re-ranking images according to their visual and semantic information and social clues. The initial results include photos or images uploaded by different social users. Usually each user contributes several images. First task is to sort out these images by interuser re-ranking. Users that have higher uploaded images with respect to the given query rank higher. Then subsequently implemented intra-user re-ranking on the ranked user's image set of inter-ranking and only the most relevant image from each user's image set is selected. These images form the final retrieved results. The inverted index structure is build for the social image data-set to accelerate the searching process. Experimental results on "Flickr" dataset shows that our social re-ranking method is efficient and effective.

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Section: Liturature Surveymentioning

confidence: 87%

A Survey on Tag Based Image Search by Social Re-ranking

2017

IJRTER

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“…However not all queries are consistently ambiguous and hence various diversification techniques might be recommended. A ranking procedure called adaptive diverse relevance ranking [6] which predicts an effective trade-off between relevance score and diversity score based on the query ambiguity level.…”

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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“…Social websites, such as Flickr, and Instagram, are visited daily by hundreds of millions of users, and the shared various multimedia resource and amounts of images are uploaded on the Internet. For example, 4.5 million new photos are added per day on Flickr, 136,000 million new photos, and 40 million photos are posted daily to the service Instagram [17]. The continuously growth of online images requires building an efficient retrieval system.…”

Section: Introductionmentioning

confidence: 99%

Social image retrieval based on topic diversity

Wang

Zhu

Qian

2021

Multimed Tools Appl

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Image search re-ranking is one of the most important approaches to enhance the text-based image search results. Extensive efforts have been dedicated to improve the accuracy and diversity of tag-based image retrieval. However, how to make the top-ranked results relevant and diverse is still a challenging problem. In this paper, we propose a novel method to diversify the retrieval results by latent topic analysis. We first employ NMF (Non-negative Matrix Factorization) Lee and Seung (Nature 401(6755):788–791, 1999) to estimate the initial relevance score to the query q. Then, the initial relevance score is fed into an adaptive multi-feature fusion model to learn the final relevance score. Next, the diversification process is conducted. We group all the images by semantic clustering and estimate the topic distribution of each cluster by topic analysis. The clusters are ranked based on the topic distribution vector and the final retrieval image list is obtained by a greedy selection mechanism based on the estimated relevances. Experimental results on the NUS-Wide dataset show the effectiveness of the proposed approach.

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Adaptive diversification for tag-based social image retrieval

Cited by 16 publications

References 24 publications

A Survey on Tag Based Image Search by Social Re-ranking

A Survey on Tag Based Image Search by Social Re-ranking

Review on Tag based Image Search by Social Re-Ranking

Social image retrieval based on topic diversity

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