On stability of signature-based similarity measures for content-based image retrieval

Beecks, Christian; Kirchhoff, Steffen; Seidl, Thomas

doi:10.1007/s11042-012-1334-3

Cited by 21 publications

(5 citation statements)

References 21 publications

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“…This supports our assumption that color is a necessary characteristic for retrieval, and that local features if considered, cannot be considered mutually independent, which makes feature signatures a great candidate for medical image retrieval. The strengths and weaknesses of the feature signature model have been investigated in various works [35][36][37] and its applicability reaches for instance from similarity search in multimedia databases [38][39][40] to scientific databases. 41,42 4 Methods…”

Section: Related Workmentioning

confidence: 99%

Content-based retrieval in videos from laparoscopic surgery

et al. 2016

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In the field of medical endoscopy more and more surgeons are changing over to record and store videos of their endoscopic procedures for long-term archival. These endoscopic videos are a good source of information for explanations to patients and follow-up operations. As the endoscope is the "eye of the surgeon", the video shows the same information the surgeon has seen during the operation, and can describe the situation inside the patient much more precisely than an operation report would do. Recorded endoscopic videos can also be used for training young surgeons and in some countries the long-term archival of video recordings from endoscopic procedures is even enforced by law. A major challenge, however, is to efficiently access these very large video archives for later purposes. One problem, for example, is to locate specific images in the videos that show important situations, which are additionally captured as static images during the procedure. This work addresses this problem and focuses on contentbased video retrieval in data from laparoscopic surgery. We propose to use feature signatures, which can appropriately and concisely describe the content of laparoscopic images, and show that by using this content descriptor with an appropriate metric, we are able to efficiently perform content-based retrieval in laparoscopic videos. In a dataset with 600 captured static images from 33 hours recordings, we are able to find the correct video segment for more than 88% of these images.Feature signatures, as for instance investigated in the work of Beecks, 1 are a compact yet efficient way of describing the content of an image. They are obtained by clustering characteristic image features into a compact feature representation that adapts to individual image contents. In this paper, we propose to utilize feature signatures over a 7-dimensional feature space including position, color, and texture information for content-based retrieval in laparoscopic videos. To this end, we investigate different distance-based similarity measures including the Earth Mover's Distance, 2 the Signature Quadratic Form Distance, 3, 4 and the Signature Matching Distance. 5 Through a large evaluation with video data from 33 hours of laparoscopic surgery we show that the proposed signature-based approaches are able to efficiently perform content-based retrieval in laparoscopic videos. Description of PurposeIn endoscopic surgery, operating surgeons usually record video segments and capture static images during the operation, which they often need after the procedure. 6 However, while the static images can be used for later discussions with patients and colleagues, and can be easily integrated into operation reports, they lack of a complete and precise information, such as movements of operation instruments and the dynamics of performed actions. The recorded videos, on the other hand, contain all these important details, since they show exactly the same images that were visible on the monitors in the operation room, which were also used by the...

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Section: Related Workmentioning

confidence: 99%

Content-based retrieval in videos from laparoscopic surgery

et al. 2016

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“…The (dis)similarity between two Feature Signatures is often determined in a distancebased manner by means of signature-based distance functions [6], such as the Earth Mover's Distance [40], the Signature Quadratic Form Distance (SQFD), or the Signature Matching Distance (SMD) [5]. In particular the latter is used as an asymmetric variant for the purpose of linking endoscopic images with video segments [4].…”

Section: Comparison To Static Content Descriptorsmentioning

confidence: 99%

“…For MIDD, CNN A , CNN G , HOG, HOF, and HMG we evaluate with Manhattan distance (L1 norm) and Euclidian distance (L2 norm). Since Feature Signatures have varying dimensionality they need an own distance measure (see [6]). We employ the Signature Matching Distance (SMD) for that purpose, using the L1 norm as ground distance.…”

Section: Retrieval Performancementioning

confidence: 99%

Video retrieval in laparoscopic video recordings with dynamic content descriptors

Schoeffmann

Husslein

Kletz

et al. 2017

Multimed Tools Appl

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In the domain of gynecologic surgery an increasing number of surgeries are performed in a minimally invasive manner. These laparoscopic surgeries require specific psychomotor skills of the operating surgeon, which are difficult to learn and teach. This is the reason why an increasing number of surgeons promote checking video recordings of laparoscopic surgeries for the occurrence of technical errors with surgical actions. This manual surgical quality assessment (SQA) process, however, is very cumbersome and timeconsuming when carried out without any support from content-based video retrieval. Appl (2018) 77:16813-16832 Descriptor) that can be effectively used to find similar segments in a laparoscopic video database and thereby help surgeons to more quickly inspect other instances of a given error scene. We evaluate the retrieval performance of MIDD with surgical actions from gynecologic surgery in direct comparison to several other dynamic content descriptors. We show that the MIDD descriptor significantly outperforms the state-of-the-art in terms of retrieval performance as well as in terms of runtime performance. Additionally, we release the manually created video dataset of 16 classes of surgical actions from medical laparoscopy to the public, for further evaluations.

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“…As opposed to the aforementioned approaches, adaptive distance-based similarity measures [3,4] such as the Earth Mover's Distance [22], the Signature Quadratic Form Distance [5], or Signature Matching Distance [2] provide the opportunity to define semantic similarity between documents in a flexible and indexable manner, even when the documents share no common entities. However, they do require a measure of similarity or dissimilarity between individual Named Entities, which can be achieved by different strategies.…”

Section: Challenge 2: Measuring Similaritymentioning

confidence: 99%

Towards Named-Entity-based Similarity Measures

Nies

Beecks

Neve

et al. 2014

Proceedings of the 7th International Workshop on Exploiting Semantic Annotations in Information Retrieval

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In this paper, we investigate challenges related to the adaptation of similarity measures used in the field of Information Retrieval to work with semantic features, i.e. Named Entities. The challenges to consider are numerous, including the accuracy of the annotation process, the adapted similarity measures, the quality of the Linked Data referred to, and the efficient access to the Semantic Web. We discuss each challenge in detail, as well as possible ways to tackle them.

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On stability of signature-based similarity measures for content-based image retrieval

Cited by 21 publications

References 21 publications

Content-based retrieval in videos from laparoscopic surgery

Content-based retrieval in videos from laparoscopic surgery

Video retrieval in laparoscopic video recordings with dynamic content descriptors

Towards Named-Entity-based Similarity Measures

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