Mining knowledge from clicks: MSR-Bing image retrieval challenge

Hua, Xian-Sheng; Ye, Ming; Li, Jin

doi:10.1109/icmew.2014.6890598

Cited by 7 publications

(2 citation statements)

References 5 publications

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“…Two possible approaches can be applied: query modeling and n-gram modeling. The most difficult aspect here is handling the scalability issue, that is, training large-number of classifiers efficiently and predicting labels over large-number of labels in real-time [9,8].…”

Section: Large-scale Query Modelingmentioning

confidence: 99%

Clickage

Hua

Yang

Wang

et al. 2013

Proceedings of the 21st ACM International Conference on Multimedia

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The semantic gap between low-level visual features and highlevel semantics has been investigated for decades but still remains a big challenge in multimedia. When "search" became one of the most frequently used applications, "intent gap", the gap between query expressions and users' search intents, emerged. Researchers have been focusing on three approaches to bridge the semantic and intent gaps: 1) developing more representative features, 2) exploiting better learning approaches or statistical models to represent the semantics, and 3) collecting more training data with better quality. However, it remains a challenge to close the gaps.In this paper, we argue that the massive amount of click data from commercial search engines provides a data set that is unique in the bridging of the semantic and intent gap. Search engines generate millions of click data (a.k.a. image-query pairs), which provide almost "unlimited" yet strong connections between semantics and images, as well as connections between users' intents and queries. To study the intrinsic properties of click data and to investigate how to effectively leverage this huge amount of data to bridge semantic and intent gap is a promising direction to advance multimedia research. In the past, the primary obstacle is that there is no such dataset available to the public research community. This changes as Microsoft has released a new large-scale real-world image click data to public. This paper presents preliminary studies on the power of large-scale click data with a variety of experiments, such as building large-scale concept detectors, tag processing, search, definitive tag detection, intent analysis, etc., with the goal to inspire deeper researches based on this dataset.

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Section: Large-scale Query Modelingmentioning

confidence: 99%

Clickage

Hua

Yang

Wang

et al. 2013

Proceedings of the 21st ACM International Conference on Multimedia

Self Cite

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“…In the multimedia community, we are heavily relying on a number of benchmarks and datasets enabling evaluation of the individual methods. Examples include MediaEval [14], TRECVID [22], MSR-Bing IRC [7], or visual sentiment ontology [1]. These benchmarks are instrumental in establishing, comparing, and improving the quality of the analysis.…”

Section: Introductionmentioning

confidence: 99%

Analytic Quality

Zahálka

Rudinac

Worring

2015

Proceedings of the 23rd ACM International Conference on Multimedia

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In this paper, we present analytic quality (AQ), a novel paradigm for the design and evaluation of multimedia analysis methods. AQ complements the existing evaluation methods based on either machine-driven benchmarks or user studies. AQ includes the notion of user insight gain and the time needed to acquire it, both critical aspects of large-scale multimedia collections analysis. To incorporate insight, AQ introduces a novel user model. In this model, each simulated user, or artificial actor, builds its insight over time, at any time operating with multiple categories of relevance. The methods are evaluated in timed sessions. The artificial actors interact with each method and steer the course by indicating relevant items throughout the session. AQ measures not only precision and recall, but also throughput, diversity of the results, and the accuracy of estimating the percentage of relevant items in the collection. AQ is shown to provide a wide picture of analytic capabilities of the evaluated methods and enumerate how their strengths differ for different purposes. The AQ time plots provide design suggestions for improving the evaluated methods. AQ is demonstrated to be more insightful than the classic benchmark evaluation paradigm both in terms of method comparison and suggestions for further design.

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Fine-Grained Image Recognition from Click-Through Logs Using Deep Siamese Network

Liu

2016

MultiMedia Modeling

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Mining knowledge from clicks: MSR-Bing image retrieval challenge

Cited by 7 publications

References 5 publications

Clickage

Clickage

Analytic Quality

Fine-Grained Image Recognition from Click-Through Logs Using Deep Siamese Network

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