Interactive search in image retrieval: a survey

Thomée, Bart; Lew, Michael S.

doi:10.1007/s13735-012-0014-4

Cited by 90 publications

(61 citation statements)

References 144 publications

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“…As computer scientists have noted, humans often are more effective than computer algorithms in searching for patterns and anomalies in imagery or visual landscapes 35,36 . Humans also are able to more easily include weighted relevance beyond simple presence-absence determinations 36,37 . Both techniques add a new dimension to traditional search methods used to discover new archaeological sites.…”

Section: Finding Undiscovered Edictsmentioning

confidence: 99%

Predictive Modelling for Archaeological Sites:Ashokan Edicts from the Indian Subcontinent

Gillespie¹,

Smith²,

Barron³

et al. 2016

Current Science

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Section: Finding Undiscovered Edictsmentioning

confidence: 99%

Predictive Modelling for Archaeological Sites:Ashokan Edicts from the Indian Subcontinent

Gillespie¹,

Smith²,

Barron³

et al. 2016

Current Science

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“…Additionally to the growing number of image collections, the semantic gap between low-level features and highlevel semantic concepts often represents great obstacles for effective image retrieval. In this scenario, interactive retrieval methods have emerged as a promising solution, based on an interactive dialog between users and CBIR systems [3].…”

Section: Related Workmentioning

confidence: 99%

“…Despite its relatively short history, relevance feedback methods evolved consistently and it remains an active research topic [3,[11][12][13]. Initially, relevance feedback was developed along the path from heuristic-based techniques to optimal learning algorithms, with early works inspired by term-weighting and relevance feedback techniques for document retrieval.…”

Section: Related Workmentioning

confidence: 99%

“…The objective is to refine the search systems based on relevance judgements provided by users. Along iterations, users assign labels to returned images (usually indicating if an image is relevant or not) [3], and the search system tunes itself in order to return more relevant images in the next iteration. The idea is to take advantage of the user perception in order to return more relevant images that better address her needs.…”

Section: Introductionmentioning

confidence: 99%

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A semi-supervised learning algorithm for relevance feedback and collaborative image retrieval

Pedronette

Calumby

Torres

2015

J Image Video Proc.

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The interaction of users with search services has been recognized as an important mechanism for expressing and handling user information needs. One traditional approach for supporting such interactive search relies on exploiting relevance feedbacks (RF) in the searching process. For large-scale multimedia collections, however, the user efforts required in RF search sessions is considerable. In this paper, we address this issue by proposing a novel semisupervised approach for implementing RF-based search services. In our approach, supervised learning is performed taking advantage of relevance labels provided by users. Later, an unsupervised learning step is performed with the objective of extracting useful information from the intrinsic dataset structure. Furthermore, our hybrid learning approach considers feedbacks of different users, in collaborative image retrieval (CIR) scenarios. In these scenarios, the relationships among the feedbacks provided by different users are exploited, further reducing the collective efforts. Conducted experiments involving shape, color, and texture datasets demonstrate the effectiveness of the proposed approach. Similar results are also observed in experiments considering multimodal image retrieval tasks.

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“…As an example, [28] considers that clicks in image search logs represent implicit relevance judgments that express the user's intent and also relations between documents. A recent survey on interactive content-based image retrieval is presented in [29]. A survey on image retrieval and automatic image annotation can be found in [30].…”

Section: Related Workmentioning

confidence: 99%

Real-time selection of video streams for live TV broadcasting based on Query-by-Example using a 3D model

Yus

Ilarri

Mena

2013

Multimed Tools Appl

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The emergence of low-cost cameras with nearly professional features in the consumer market represents a new important source of video information. For example, using an increasing number of these cameras in live TV broadcastings enables obtaining varied contents without affecting the production costs. However, searching for interesting shots (e.g., a certain view of a specific car in a race) among many video sources in real-time can be difficult for a Technical Director (TD). So, TDs require a mechanism to easily and precisely represent the kind of shot they want to obtain abstracting them from the need to be aware of all the views provided by the cameras.In this paper we present our proposal to help a TD to visually define, using an interface for the definition of 3D scenes, an interesting sample view of one or more objects in the scenario. We recreate the views of the cameras in a 3D engine and apply 3D geometric computations on their virtual view, instead of analyzing the real images they provide, to enable an efficient and precise realtime selection. Specifically, our system computes a similarity measure to rank the candidate cameras. Moreover, we present a prototype of the system and an experimental evaluation that shows the interest of our proposal.

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Interactive search in image retrieval: a survey

Cited by 90 publications

References 144 publications

Predictive Modelling for Archaeological Sites:Ashokan Edicts from the Indian Subcontinent

Predictive Modelling for Archaeological Sites:Ashokan Edicts from the Indian Subcontinent

A semi-supervised learning algorithm for relevance feedback and collaborative image retrieval

Real-time selection of video streams for live TV broadcasting based on Query-by-Example using a 3D model

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