Image Retrieval Interfaces: A User Perspective

Eakins, John P.; Briggs, Pam; Burford, Bryan

doi:10.1007/978-3-540-27814-6_73

Cited by 34 publications

(32 citation statements)

References 6 publications

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“…The easiest way to search similar images is different under different searching scenarios or users' searching needs; therefore, the corresponding user interfaces should be designed to fit users' various searching needs (Zhang, 2010). For image databases user, the ability to retrieve images based on their semantic content is important (Eakins et al, 2004). Besides, the user interface created for image searching may need to provide additional functionality to embrace the rich descriptions of the image, as opposed to the limited information provided by the usually short queries.…”

Section: Interactive Interfacementioning

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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Section: Interactive Interfacementioning

confidence: 99%

Towards Semantic User Query: A Review

Wang¹,

Chai²

2015

Journal of Computer Science

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“…Survey questionnaire methods have been widely used in several studies for a long time [8][9][10][11]. Especially, systems involving user interactivity utilize survey methods for usability studies [8].…”

Section: Review Of Literaturementioning

confidence: 99%

“…Jaimes [10] studied human factors, which influence automatic content-based retrieval systems, such as human memory, context and subjectivity. Eakins et al [11] used online questionnaire method in order to improve user interface of CBIR applications. Halvey and Keane in [12], studied log statistics of YouTube to provide an analysis of user's interaction with video search engines.…”

Section: Review Of Literaturementioning

confidence: 99%

System profiles in content-based image indexing and retrieval

Guldogan

Gabbouj

2009

SIViP

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In this paper, a novel study on system profiles and adaptation of parameters for end-users of content-based indexing and retrieval (CBIR) applications are presented. The main objective of the study is improving the overall CBIR application performance in different hardware platforms having different technical capabilities and conditions. We define CBIR system profiles in terms of hardware and system platform attributes and propose CBIR parameters for each profile. Hence, the study consists of two main parts: system profiling and adaptation of indexing and retrieval parameters for each profile. The proposed CBIR parameters are appropriate configurations for optimal CBIR use on every platform. The proposed parameters for each system profile are assessed over a large set of experiments. Experimental studies show that the proposed parameters for each system profile have satisfactory semantic retrieval performance, with reduced computational complexity and storage space requirement. 45 to 78% improvement is achieved in the computational complexity of the retrieval process depending on the profile.Keywords Content-based indexing and retrieval · User hardware system profiles · Indexing and retrieval parameters · Optimization of indexing and retrieval performance

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“…The importance of text for video retrieval has not gone unnoticed in the TRECVID conferences and was pointed out by Hauptmann [11], amongst others. Eakins pointed out in [6] that users of image retrieval systems rate text entry interfaces higher than CBIR techniques such as QBE. This point should be taken into account when designing user interfaces of retrieval systems.…”

Section: Textual Queriesmentioning

confidence: 99%

Assessing user behaviour in news video retrieval

Hollink

Nguyen

Koelma

et al. 2005

IEE Proc., Vis. Image Process.

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Abstract. In this paper we present the results of a study in which we assess search behaviour of people querying a news archive using an interactive video retrieval system. 242 Search sessions by 39 participants on 24 topics were analysed. Before, during and after the study, participants filled in questionnaires about their expectations of a search. The questionnaire data, logged user actions on the system, queries formulated by users, and a quality measure of each search were studied. The results of the study show that topics concerning 'specific' people or objects were better retrieved than topics concerning 'general' objects and scenes. Users were able to estimate the overall quality of a search but did not know when the optimal result was reached within the search process. Analysis of the results at various stages in the retrieval process suggests that retrieval based on transcriptions of the speech in video data adds more to the average precision of the result than content-based image retrieval based on low-level visual features. The latter is particularly useful in providing the user with an overview of the dataset and thus an indication of the success of a search. Based on the results we discuss implications for the design of user interfaces of video retrieval systems.

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Image Retrieval Interfaces: A User Perspective

Cited by 34 publications

References 6 publications

Towards Semantic User Query: A Review

Towards Semantic User Query: A Review

System profiles in content-based image indexing and retrieval

Assessing user behaviour in news video retrieval

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