Picadomo: Faceted Image Browsing for Mobile Devices

Hub, Adrian; Blank, Daniel; Henrich, Andreas; Müller, Wolfgang

doi:10.1109/cbmi.2009.34

Cited by 7 publications

(3 citation statements)

References 5 publications

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“…In the context of exploration of documents, the use of faceted search [60] blends search and browsing [28,37], wherein a user navigates through a document collection based on filters and attributes, is a popular one. Systems such as Querium [20] have investigated the use of faceted search in a collaborative setting, combining it with relevance feedback, query fusion, and a session-based model for search histories.…”

Section: Faceted Search and Explorationmentioning

confidence: 99%

Don't Just Swipe Left, Tell Me Why

Beltrán¹,

Huang

Abouzied

et al. 2017

Proceedings of the 22nd International Conference on Intelligent User Interfaces

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Despite several advances in information retrieval systems and user interfaces, the specification of queries over text-based document collections remains a challenging problem. Query specification with keywords is a popular solution. However, given the widespread adoption of gesture-driven interfaces such as multitouch technologies in smartphones and tablets, the lack of a physical keyboard makes query specification with keywords inconvenient. We present BINGO, a novel gestural approach to querying text databases that allows users to refine their queries using a swipe gesture to either "like" or "dislike" candidate documents as well as express the reasons they like or dislike a document by swiping through automatically generated "reason bins". Such reasons refine a user's query with additional keywords. We present an online and efficient bin generation algorithm that presents reason bins at gesture articulation. We motivate and describe BINGO's unique interface design choices. Based on our analysis and user studies, we demonstrate that query specification by swiping through reason bins is easy and expressive.

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Section: Faceted Search and Explorationmentioning

confidence: 99%

Don't Just Swipe Left, Tell Me Why

Beltrán¹,

Huang

Abouzied

et al. 2017

Proceedings of the 22nd International Conference on Intelligent User Interfaces

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“…So far, only few mobile-based applications can be found for image retrieval and annotation. Most of them serve as local image applications for finding locally stored images on the mobile phone itself [4]. A few available web-based systems [5] include a system to search buildings and pictures of art galleries [6].…”

Section: Introductionmentioning

confidence: 99%

MOSIR: Mobile-Based Segment and Image Retrieval

Nakagawa

Kutics

Phyu

2011

2011 7th International Conference on Wireless Communications, Networking and Mobile Computing

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This paper proposes a content-based image retrieval system for mobile phones, named MOSIR. The system facilitates instantaneous search on mobile phones for images similar to photos taken via phone cameras. Edge-based and color-layout features are used to find similar images. The system also enables region-based queries by detecting salient regions and extracting their features. To overcome security problems, the system applies both self-defined email and web application software. The system overcomes limitation of display panels and control key in common mobile phones by using Flash-Lite as a new user interface approach to view images of different sizes and contexts. The system was tested with 2,250 images, and results were satisfying. Therefore, MOSIR is a promising system to perform instant image search on mobile phones. Currently, it is targeted for common mobile phones. Eventually, MOSIR is aimed to support smart phones and retrieval of semantically related information to resulted images.

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“…Mobile devices are equipped with digital cameras and memory cards. Picadamo technique combines content-based image retrieval and faceted search on mobile devices and it is designed for finding images with desired visual properties or other known meta data [20]. Hangjung Zo and K. Ramamurthy proposed Consumer Selection of E-Commerce Websites in a B2C Environment: A Discrete Decision Choice Model.…”

Section: Literature Surveymentioning

confidence: 99%

Dynamic Facet Ordering for Faceted Product Search Engines

Bhagyashree¹,

Bindu²,

K³

et al. 2018

Nciccnda

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Faceted browsing is widely used in Web shops and product comparison sites. In these cases, a fixed ordered list of facets is often employed. This approach suffers from two main issues. First, one needs to invest a significant amount of time to devise an effective list. Second, with a fixed list of facets it can happen that a facet becomes useless if all products that match the query are associated to that particular facet. In this work, we present a framework for dynamic facet ordering in e-commerce. Based on measures for specificity and dispersion of facet values, the fully automated algorithm ranks those properties and facets on top that lead to a quick drill-down for any possible target product. In contrast to existing solutions, the framework addresses e-commerce specific aspects, such as the possibility of multiple clicks, the grouping of facets by their corresponding properties, and the abundance of numeric facets. In a large-scale simulation and user study, our approach was, in general, favorably compared to a facet list created by domain experts, a greedy approach as baseline, and a state-of-the-art entropy-based solution. In this work use different types of metrics to score qualitative and numerical properties. For property ordering we want to rank properties descending on their impurity, promoting more selective facets that will lead to a quick drill-down of the results

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Picadomo: Faceted Image Browsing for Mobile Devices

Cited by 7 publications

References 5 publications

Don't Just Swipe Left, Tell Me Why

Don't Just Swipe Left, Tell Me Why

MOSIR: Mobile-Based Segment and Image Retrieval

Dynamic Facet Ordering for Faceted Product Search Engines

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