Improving image search effectiveness by integrating contextual information

2013 ◽  
Author(s):  
Ghada Feki ◽  
Amel Ksibi ◽  
Anis Ben Ammar ◽  
Chokri Ben Amar
Keyword(s):  
Contextual Information ◽  
Image Search ◽  
Search Effectiveness

A Medieval Burial from Graham Street, Leith, Edinburgh

2003 ◽  
Vol 25 (2) ◽  
pp. 165-169
Author(s):  
Paul R. J. Duffy ◽  
Olivia Lelong
Keyword(s):  
Contextual Information ◽  
Human Remains ◽  
Housing Development ◽  
Archaeological Research ◽  
Archaeological Excavation ◽  
Radiocarbon Dates ◽  
Physical Evidence ◽  
Research Division ◽  
The North ◽  
The Individual

Summary An archaeological excavation was carried out at Graham Street, Leith, Edinburgh by Glasgow University Archaeological Research Division (GUARD) as part of the Historic Scotland Human Remains Call-off Contract following the discovery of human remains during machine excavation of a foundation trench for a new housing development. Excavation demonstrated that the burial was that of a young adult male who had been interred in a supine position with his head orientated towards the north. Radiocarbon dates obtained from a right tibia suggest the individual died between the 15th and 17th centuries AD. Little contextual information exists in documentary or cartographic sources to supplement this scant physical evidence. Accordingly, it is difficult to further refine the context of burial, although a possible link with a historically attested siege or a plague cannot be discounted.

Customized Concept Learning in Image Search from Photo Sharing Websites

2012 ◽  
Vol 2 (2) ◽  
pp. 114-115
Author(s):  
Ch. priyanka Ch. priyanka ◽  
◽  
Dr P.Govardhan Dr P.Govardhan
Keyword(s):  
Concept Learning ◽  
Image Search ◽  
Photo Sharing

Pencarian File Gambar Berdasarkan Dominasi Warna

2011 ◽  
Vol 2 (1) ◽  
Author(s):  
Vina Chovan Epifania ◽  
Eko Sediyono
Keyword(s):  
Digital Image ◽  
Retrieval System ◽  
Color Image ◽  
Color Space ◽  
Image Data ◽  
Image Search ◽  
Test Results ◽  
Application Form ◽  
Space Model ◽  
Image File

Abstract. Image File Searching Based on Color Domination. One characteristic of an image that can be used in image searching process is the composition of the colors. Color is a trait that is easily seen by man in the picture. The use of color as a searching parameter can provide a solution in an easier searching for images stored in computer memory. Color images have RGB values that can be computed and converted into HSL color space model. Use of HSL images model is very easy because it can be calculated using a percent, so that in each pixel of the image can be grouped and named, this can give a dominant values of the colors contained in one image. By obtaining these values, the image search can be done quickly just by using these values to a retrieval system image file. This article discusses the use of the HSL color space model to facilitate the searching for a digital image in the digital image data warehouse. From the test results of the application form, a searching is faster by using the colors specified by the user. Obstacles encountered were still searching with a choice of 15 basic colors available, with a limit of 33% dominance of the color image search was not found. This is due to the dominant color in each image has the most dominant value below 33%.   Keywords: RGB, HSL, image searching Abstrak. Salah satu ciri gambar yang dapat dipergunakan dalam proses pencarian gambar adalah komposisi warna. Warna adalah ciri yang mudah dilihat oleh manusia dalam citra gambar. Penggunaan warna sebagai parameter pencarian dapat memberikan solusi dalam memudahkan pencarian gambar yang tersimpan dalam memori komputer. Warna gambar memiliki nilai RGB yang dapat dihitung dan dikonversi ke dalam model HSL color space. Penggunaan model gambar HSL sangat mudah karena dapat dihitung dengan menggunakan persen, sehingga dalam setiap piksel gambar dapat dikelompokan dan diberi nama, hal ini dapat memberikan suatu nilai dominan dari warna yang terdapat dalam satu gambar. Dengan diperolehnya nilai tersebut, pencarian gambar dapat dilakukan dengan cepat hanya dengan menggunakan nilai tersebut pada sistem pencarian file gambar. Artikel ini membahas tentang penggunaan model HSL color space untuk mempermudah pencarian suatu gambar digital didalam gudang data gambar digital. Dari hasil uji aplikasi yang sudah dibuat, diperoleh pencarian yang lebih cepat dengan menggunakan pilihan warna yang ditentukan sendiri oleh pengguna. Kendala yang masih dijumpai adalah pencarian dengan pilihan 15 warna dasar yang tersedia, dengan batas dominasi warna 33% tidak ditemukan gambar yang dicari. Hal ini disebabkan warna dominan disetiap gambar kebanyakan memiliki nilai dominan di bawah 33%. Kata Kunci: RGB, HSL, pencarian gambar

"He seems odd": The effects of risk-irrelevant information and actuarial risk estimates on mock jurors' perceptions of sexual recidivism risk

2020 ◽  
Author(s):  
Jennifer Kamorowski ◽  
Karl Ask ◽  
Maartje Schreuder ◽  
Marko Jelicic ◽  
Corine de Ruiter
Keyword(s):  
Contextual Information ◽  
Individual Characteristics ◽  
Risk Estimate ◽  
Case Vignette ◽  
Future Research ◽  
Sexual Recidivism ◽  
Mock Jurors ◽  
Related Factors ◽  
Risk Estimates ◽  
Actuarial Risk

Previous research has shown that mock and actual jurors give little weight to actuarial sexual offending recidivism risk estimates when making decisions regarding civil commitment for so-called sexually violent predators (SVPs). We hypothesized that non-risk related factors, such as irrelevant contextual information and jurors’ information-processing style, would influence mock jurors’ perceptions of sexual recidivism risk. This preregistered experimental study examined the effects of mock jurors’ (N = 427) need for cognition (NFC), irrelevant contextual information in the form of the offender’s social attractiveness, and an actuarial risk estimate on mock jurors’ estimates of sexual recidivism risk related to a simulated SVP case vignette. Mock jurors exposed to negative risk-irrelevant characteristics of the offender estimated sexual recidivism risk as higher than mock jurors exposed to positive information about the offender. However, this effect was no longer significant after mock jurors had reviewed Static-99R actuarial risk estimate information. We found no support for the hypothesis that the level of NFC moderates the relationship between risk-irrelevant contextual information and risk estimates. Future research could explore additional individual characteristics or attitudes among mock jurors that may influence perceptions of sexual recidivism risk and insensitivity to actuarial risk estimates.

Minimizing Bias in Forensic Handwriting Examinations

2015 ◽  
Vol 25 ◽  
pp. 17-26 ◽  
Author(s):  
L. C. Alewijnse ◽  
E.J.A.T. Mattijssen ◽  
R.D. Stoel
Keyword(s):  
Decision Making ◽  
Information Management ◽  
Contextual Information ◽  
Context Information ◽  
Decision Making Process ◽  
Potential Bias ◽  
Potential Influence ◽  
Management Procedures

The purpose of this paper is to contribute to the increasing awareness about the potential bias on the interpretation and conclusions of forensic handwriting examiners (FHEs) by contextual information. We briefly provide the reader with an overview of relevant types of bias, the difficulties associated with studying bias, the sources of bias and their potential influence on the decision making process in casework, and solutions to minimize bias in casework. We propose that the limitations of published studies on bias need to be recognized and that their conclusions must be interpreted with care. Instead of discussing whether bias is an issue in casework, the forensic handwriting community should actually focus on how bias can be minimized in practice. As some authors have already shown (e.g., Found & Ganas, 2014), it is relatively easy to implement context information management procedures in practice. By introducing appropriate procedures to minimize bias, not only forensic handwriting examination will be improved, it will also increase the acceptability of the provided evidence during court hearings. Purchase Article - $10

Multi-level 3D Densenets for False-positive Reduction in Lung Nodule Detection Based on Chest Computed Tomography

2020 ◽  
Vol 16 (8) ◽  
pp. 1004-1021
Author(s):  
Xiaoqi Lu ◽  
Yu Gu ◽  
Lidong Yang ◽  
Baohua Zhang ◽  
Ying Zhao ◽  
...  
Keyword(s):  
False Positive ◽  
Contextual Information ◽  
Lung Nodule ◽  
Positive Sample ◽  
Lung Nodules ◽  
Lung Nodule Detection ◽  
Nodule Detection ◽  
Level Information ◽  
Multi Level ◽  
Cade System

Objective: False-positive nodule reduction is a crucial part of a computer-aided detection (CADe) system, which assists radiologists in accurate lung nodule detection. In this research, a novel scheme using multi-level 3D DenseNet framework is proposed to implement false-positive nodule reduction task. Methods: Multi-level 3D DenseNet models were extended to differentiate lung nodules from falsepositive nodules. First, different models were fed with 3D cubes with different sizes for encoding multi-level contextual information to meet the challenges of the large variations of lung nodules. In addition, image rotation and flipping were utilized to upsample positive samples which consisted of a positive sample set. Furthermore, the 3D DenseNets were designed to keep low-level information of nodules, as densely connected structures in DenseNet can reuse features of lung nodules and then boost feature propagation. Finally, the optimal weighted linear combination of all model scores obtained the best classification result in this research. Results: The proposed method was evaluated with LUNA16 dataset which contained 888 thin-slice CT scans. The performance was validated via 10-fold cross-validation. Both the Free-response Receiver Operating Characteristic (FROC) curve and the Competition Performance Metric (CPM) score show that the proposed scheme can achieve a satisfactory detection performance in the falsepositive reduction track of the LUNA16 challenge. Conclusion: The result shows that the proposed scheme can be significant for false-positive nodule reduction task.

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