Grouping web image search result

Wang, Xinjing; Ma, Wei‐Ying; He, Qi-Cai; Li, Xing

doi:10.1145/1027527.1027632

Cited by 21 publications

(13 citation statements)

References 4 publications

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“…Similar ideas of generating TOC were addressed by Wang et al [8] and the IGroup system [3]. Both these work group web images to improve the search result.…”

Section: Introductionmentioning

confidence: 89%

“…Both these work group web images to improve the search result. Wang et al [8] made use of the visual context of an image while IGroup identifies salient key phrases from image surrounding texts. They attempted to structuralize image search results to facilitate user browsing, but neither of them identified the most interested aspects, nor did they re-rank image search results to punish irrelevance and encourage diversity.…”

Section: Introductionmentioning

confidence: 99%

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Diversifying landmark image search results by learning interested views from community photos

Ren

Wang

et al. 2010

Proceedings of the 19th International Conference on World Wide Web

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In this paper, we demonstrate a novel landmark photo search and browsing system, Agate, which ranks landmark image search results considering their relevance, diversity and quality. Agate learns from community photos the most interested aspects and related activities of a landmark, and generates adaptively a Table of Content (TOC) as a summary of the attractions to facilitate user browsing. Image search results are thus re-ranked with the TOC so as to ensure a quick overview of the attractions of the landmarks. A novel non-parametric TOC generation and re-ranking algorithm, MoM-DPM Sets, is proposed as the key technology of Agate. Experimental results based on human evaluation show the effectiveness of our model and user preference for Agate.

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“…Similar ideas of generating TOC were addressed by Wang et al [8] and the IGroup system [3]. Both these work group web images to improve the search result.…”

Section: Introductionmentioning

confidence: 89%

Section: Introductionmentioning

confidence: 99%

Diversifying landmark image search results by learning interested views from community photos

Ren

Wang

et al. 2010

Proceedings of the 19th International Conference on World Wide Web

Self Cite

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“…From an application point of view, researchers from the multimedia community dedicated more attention for Web image clustering. In fact, the www.ijacsa.thesai.org unsupervised learning (clustering) techniques are valuable when meta-data is collected/extracted in addition to visual descriptors [33,34,37]. Unsupervised learning usually serves to recognize new images and assign them to some predefined categories before proceeding with the retrieval phase.…”

Section: A Supervised and Unsupervised Learningmentioning

confidence: 99%

A Survey on Content-based Image Retrieval

Ismail¹

2017

ijacsa

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Abstract-The widespread of smart devices along with the exponential growth of virtual societies yield big digital image databases. These databases can be counter-productive if they are not coupled with efficient Content-Based Image Retrieval (CBIR) tools. The last decade has witnessed the introduction of promising CBIR systems and promoted applications in various fields. In this article, a survey on state of the art content based image retrieval including empirical and theoretical work is proposed. This work also includes publications that cover research aspects relevant to CBIR area. Namely, unsupervised and supervised learning and fusion techniques along with lowlevel image visual descriptors have been reported. Moreover, challenges and applications that emerged to support CBIR research have been discussed in this work.

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“…Following the first approach, clustering is directly performed on image information, which can be visual or/and textual. Within this context, [9] extract codewords based on image analysis, which are then ranked based on a regression model. The salient visual phrases are then used to gather images and form clusters in a monothetic like-wise strategy.…”

Section: Related Workmentioning

confidence: 99%

Using Text-Based Web Image Search Results Clustering to Minimize Mobile Devices Wasted Space-Interface

Moreno

Dias

2013

Lecture Notes in Computer Science

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Abstract. The recent shift in human-computer interaction from desktop to mobile computing fosters the needs of new interfaces for web image search results exploration. In order to leverage users' efforts, we present a set of state-of-the-art ephemeral clustering algorithms, which allow to summarize web image search results into meaningful clusters. This way of presenting visual information on mobile devices is exhaustively evaluated based on two main criteria: clustering accuracy, which must be maximized, and wasted space-interface, which must be minimized. For the first case, we use a broad set of metrics to evaluate ephemeral clustering over a public golden standard data set of web images. For the second case, we propose a new metric to evaluate the mismatch of the used space-interface between the ground truth and the cluster distribution obtained by ephemeral clustering. The results evidence that there exist high divergences between clustering accuracy and used space maximization. As a consequence, the trade-off of cluster-based exploration of web image search results on mobile devices is difficult to define, although our study evidences some clear positive results.

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Grouping web image search result

Cited by 21 publications

References 4 publications

Diversifying landmark image search results by learning interested views from community photos

Diversifying landmark image search results by learning interested views from community photos

A Survey on Content-based Image Retrieval

Using Text-Based Web Image Search Results Clustering to Minimize Mobile Devices Wasted Space-Interface

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