Image Search Reranking with Relevance, Diversity and Topic Coverage

Lin, Xuefei; Zhang, Tian

doi:10.1145/3007669.3007738

Cited by 1 publication

(3 citation statements)

References 12 publications

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“…The images are re-ranked by random walk on the constructed graph , and the diversity is achieved by setting the transition probability of two images within the same topic higher than those within different topics. Lin et al [24] first cluster the images to find the latent topics and then propose a greedy selection algorithm to fuse the relevance score, visual similarity and level of topic coverage. In [21], the authors propose three clustering algorithms to diversify the retrieval results from three different emphases.…”

Section: Retrieval Results Diversificationmentioning

confidence: 99%

“…The diversity is achieved by recurrently selecting images that are dissimilar with the items appearing before. -DRCR: Diverse, Relevance, Topic Coverage, representativeness Ranking [24]. A DRCR score is defined by taking the relevance, topic coverage, and diversity into consideration simultaneously, the final results are obtained by greedy selection.…”

Section: Methodsmentioning

confidence: 99%

“…We will discuss above four parameters and topic analysis method in Section 5. To make fair comparisons for six methods, we use the parameters that the corresponding paper suggests for MMS [37], DRCR [24], TDR [30], VDR [21], USDM [55], and LUPI [52].…”

Section: Methodsmentioning

confidence: 99%

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Social image retrieval based on topic diversity

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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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Section: Retrieval Results Diversificationmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%