A classification scheme for user intentions in image search

Lux, Mathias; Kofler, Christoph; Marques, Oge

doi:10.1145/1753846.1754078

Cited by 23 publications

(43 citation statements)

References 5 publications

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“…Human interactions with computers never take place in a vacuum, but always occur in response to some user need or goal, and image search is no exception [13]. As with any information service, to provide the best possible service it is crucial to understand the underlying goals behind user behavior, motivating our final research question:…”

Section: Rq 2 How Does Image Search Behavior Differ Depending On Quementioning

confidence: 99%

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A Large-Scale Study of User Image Search Behavior on the Web

Park

O’Hare

Schifanella³

et al. 2015

Proceedings of the 33rd Annual ACM Conference on Human Factors in Computing Systems

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In this study, we analyze user image search behavior on a large-scale query log from Yahoo Image Search, based on the hypothesis that behavior is dependent on query type. We categorize queries using two orthogonal taxonomies (subjectbased and facet-based) and identify important query types at the intersection of these taxonomies. We study user search behavior on a large-scale set of search sessions for each query type, examining characteristics of sessions, query reformulation patterns, click patterns, and page view patterns. We identify important behavioral differences across query types, in particular showing that some query types are more exploratory, while others correspond to focused search. We also supplement our study with a survey to link the behavioral differences to image search intent. Our findings shed light on the importance of considering query categories to better understand user behavior on image search platforms.

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Section: Rq 2 How Does Image Search Behavior Differ Depending On Quementioning

confidence: 99%

“…The general goals and tasks that motivate search provide the context for search behavior. Broder [3] proposed a taxonomy of intent for web search, which was adapted to image search by Lux et al [13].…”

Section: Related Workmentioning

confidence: 99%

A Large-Scale Study of User Image Search Behavior on the Web

Park

O’Hare

Schifanella³

et al. 2015

Proceedings of the 33rd Annual ACM Conference on Human Factors in Computing Systems

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show abstract

“…A substitute of measuring user intentions is to study user search queries and browsing behavior for text-based search (Broder, 2002;Jansen et al, 2008;Yin and Shah, 2010;Kumar and Tomkins, 2010) and image search (Kofler and Lux, 2009;Lux et al, 2010).…”

Section: User Intentmentioning

confidence: 99%

Towards Semantic User Query: A Review

Wang¹,

Chai²

2015

Journal of Computer Science

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This paper attempts to discuss the image query mechanisms and user needs for image retrieval. The explosive growth of image data leads to the need of research and development of Image retrieval. Image retrieval researches are moving from keyword, to low level features and to semantic features. Drive towards semantic features is due to the problem of the keywords which can be very subjective and time consuming while low level features cannot always describe high level concepts in the users' mind. This paper also highlights both the already addressed and outstanding issues.

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“…Intent has acquired more and more importance in multimedia research in the last years and multiple studies have tried to make the text retrieval approach usable for multimedia [17,13,15]. For example, Lux et al [19] attempted to find possible intent categories for image retrieval similar to the approach presented in [8]. However, these intent-based papers exploit intent in the context of images.…”

Section: Related Workmentioning

confidence: 99%

Exploitation of producer intent in relation to bandwidth and QoE for online video streaming services

Riegler

Calvet

et al. 2015

Proceedings of the 25th ACM Workshop on Network and Operating Systems Support for Digital Audio and Video

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This paper is the product of recent advances in research on users' intent during multimedia content retrieval. Our goal is to save bandwidth while streaming video clips from a browsable on-demand service, while maintaining or even improving the users' quality of experience (QoE). Understanding user intent allows us to predict whether streaming a particular video in a low quality constitutes a reduced QoE for a user. However, many VoD streaming services today are used by users for a wide variety of reasons, meaning that user intent cannot be inferred from their use of the service alone. However, our investigation demonstrates that user intent does in most cases coincide with producer intent. We can also demonstrate that the latter can be inferred from the content itself as well as associated metadata. By transitivity, we can choose a default video quality that satisfies the users QoE in the majority of cases.

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A classification scheme for user intentions in image search

Cited by 23 publications

References 5 publications

A Large-Scale Study of User Image Search Behavior on the Web

A Large-Scale Study of User Image Search Behavior on the Web

Towards Semantic User Query: A Review

Exploitation of producer intent in relation to bandwidth and QoE for online video streaming services

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