Query difficulty estimation via relevance prediction for image retrieval

Jia, Qianghuai; Tian, Xinmei

doi:10.1016/j.sigpro.2014.07.018

Cited by 6 publications

(3 citation statements)

References 24 publications

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“…The authors adapt the visual content representation to the class predicted in the first stage. Jia et al [33,34] introduced a post-retrieval predictor that divides the retrieved images into pseudo-positive and pseudo-negative via pseudo-relevance feedback. Next, a voting scheme is applied to label the images as relevant or not.…”

Section: Qpp In Image Searchmentioning

confidence: 99%

“…Most of the above studies, e.g. [33,34,45,70,71,74], predict the performance of text queries in CBIR. Our study is among the few works [42,46,68,72] studying the performance of image queries.…”

Section: Qpp In Image Searchmentioning

confidence: 99%

“…The importance of QPP has been widely recognized in text retrieval [3, 10-13, 18, 24-26, 35, 39, 40, 43, 44, 51, 54-56, 58, 63-65, 69, 76, 77], being an actively studied task nowadays [1, 2, 9, 14-16, 22, 32, 60]. However, in the context of content-based image retrieval (CBIR), the QPP task received comparably lower attention from the research community, with only a few studies addressing the topic [33,34,42,45,46,68,[70][71][72]74]. Furthermore, only a handful of papers [42,46,68,72] study query difficulty prediction in the queryby-example scenario, where the query is an image.…”

Section: Introductionmentioning

confidence: 99%

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iQPP: A Benchmark for Image Query Performance Prediction

Poesina

Ionescu

Mothe

2023

Proceedings of the 46th International ACM SIGIR Conference on Research and Development in Information Retrieval

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To date, query performance prediction (QPP) in the context of contentbased image retrieval remains a largely unexplored task, especially in the query-by-example scenario, where the query is an image. To boost the exploration of the QPP task in image retrieval, we propose the first benchmark for image query performance prediction (iQPP). First, we establish a set of four data sets (PASCAL VOC 2012, Caltech-101, ROxford5k and RParis6k) and estimate the ground-truth difficulty of each query as the average precision or the precision@𝑘, using two state-of-the-art image retrieval models. Next, we propose and evaluate novel pre-retrieval and post-retrieval query performance predictors, comparing them with existing or adapted (from text to image) predictors. The empirical results show that most predictors do not generalize across evaluation scenarios. Our comprehensive experiments indicate that iQPP is a challenging benchmark, revealing an important research gap that needs to be addressed in future work. We release our code and data as open source at https://github.com/Eduard6421/iQPP, to foster future research.

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Section: Qpp In Image Searchmentioning

confidence: 99%