Combining heterogeneous sources in an interactive multimedia content retrieval model

Schneider, Julián Moreno; Martı́nez, Paloma; Martínez-Fernández, José Luis

doi:10.1016/j.eswa.2016.10.049

Cited by 11 publications

(9 citation statements)

References 45 publications

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“…The text contained in an image is extracted using Tesseract 14 . This is a standard Optical Character Recognition Tool used in many other information retrieval studies as [34][35][36]. The defects highlighted by the study [37] requiring prior training will have been limited by a prior filtering of the scanned files according to their qualities.…”

Section: Application With Textual Contentmentioning

confidence: 99%

Key issues for a manufacturing data query system based on graph

Kim

Yahia

Segonds

et al. 2021

Int J Interact Des Manuf

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Manufacturing industry data are distributed, heterogeneous and numerous, resulting in different challenges including fast, exhaustive and relevant querying of data. In order to provide an innovative answer to this challenge, the authors consider an information retrieval system based on a graph database. In this paper, the authors focus on determining the key issues to consider in this context. The authors define a three-step methodology using root causes analysis. This methodology is then applied to a data set and queries representative of an industrial use case. As a result, the authors list four main issues: (i) semantic extension of keyword search, (ii) the treatment of syntactic heterogeneity contained in unstructured data, (iii) the results treatment by relevance order and (iv) the detection of relationships between a priori unrelated data. The authors conclude by discussing potential resolutions of these four issues, suggest adapting the methodology used in the paper to evaluate a future proposal, and finally open the possibility of using the results beyond the manufacturing domain.

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Section: Application With Textual Contentmentioning

confidence: 99%

Key issues for a manufacturing data query system based on graph

Kim

Yahia

Segonds

et al. 2021

Int J Interact Des Manuf

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“…These types are vertical, horizontal, diagonal with 45 and 135 degrees, and non-directional edges. Sometimes the sub-image can be categorized as non-edge block [9]. There are future hopes and attempts to develop a joint representation of EHD and CLD [10].…”

Section: Lucene Image Retrievalmentioning

confidence: 99%

A new approach for content-based image retrieval for medical applications using low-level image descriptors

Shatnawi¹,

Al-Rousan²,

Amareen³

2020

IJECE

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<p>Content based image retrieval (CBIR) has become an important factor in medical imaging research and is obtaining a great success. More applications still need to be developed to get more powerful systems for better image similarity matching, and as a result getting better image retrieval systems. This research focuses on implementing low-level descriptors to maximize the quality of the retrieval of medical images. Such a research is supposed to set a better result in terms of image similarity matching. In this research a system that uses low-level descriptors is introduced. Three descriptors have been developed and applied in an attempt to increase the accuracy of image matching. The final results showed a qualified system in medical images retrieval specially that the low-level image descriptors have not been used yet in the image similarity matching in the medical field.</p>

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“…The challenges of providing sufficient training examples and defining effective search queries in words has seen the investigation of alternatives to text-based queries which use richer forms of query data, and direct comparisons between query data and target videos via more general purpose feature-based similarity measures which bypass concept learning altogether. Examples include: query-by-example where users can provide an example image or video defining their query (e.g., Snoek et al (2007); Yang et al (2013)), queryby-sketch where users can draw their query (e.g., Hu et al (2007)), query-by-object where users can provide an image of an object defining their query (e.g., Sivic & Zisserman (2003)), and combinations thereof (e.g., Moreno-Schneider et al (2017)).…”

Section: Content-based Video Retrievalmentioning

confidence: 99%

The development of a video retrieval system using a clinician-led approach

Darby

Sánchez

Bew

et al. 2020

Expert Systems with Applications

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Patient video taken at home can provide valuable insights into the recovery progress during a programme of physical therapy, but is very time consuming for clinician review. Our work focussed on i) enabling any patient to share information about progress at home, simply by sharing video and ii) building intelligent systems to support Physical Therapists (PTs) in reviewing this video data and extracting the necessary detail. This paper reports the development of the system, appropriate for future clinical use without reliance on a technical team, and the clinician involvement in that development. We contribute an interactive contentbased video retrieval system that significantly reduces the time taken for clinicians to review videos, using human head movement as an example. The system supports query-by-movement (clinicians move their own body to define search queries) and retrieves the essential fine-grained movements needed for clinical interpretation. This is done by comparing sequences of image-based pose estimates (here head rotations) through a distance metric (here Fréchet distance) and presenting a ranked list of similar movements to clinicians for review. In contrast to existing intelligent systems for retrospective review of human movement, the system supports a flexible analysis where clinicians can look for any movement that interests them. Evaluation by a group of PTs with expertise in training movement control showed that 96% of all relevant movements were identified with time savings of as much as 99.1% compared to reviewing target videos in full. The novelty of this contribution includes retrospective progress monitoring that preserves context through video, and content-based video retrieval that supports both fine-grained human actions and queryby-movement. Future research, including large clinician-led studies, will refine the technical aspects and explore the benefits in terms of patient outcomes, PT time, and financial savings over the course of a programme of therapy. It is anticipated that this clinician-led approach will mitigate the reported slow clinical uptake of technology with resulting patient benefit.

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Combining heterogeneous sources in an interactive multimedia content retrieval model

Cited by 11 publications

References 45 publications

Key issues for a manufacturing data query system based on graph

Key issues for a manufacturing data query system based on graph

A new approach for content-based image retrieval for medical applications using low-level image descriptors

The development of a video retrieval system using a clinician-led approach

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