scholarly journals Reducing image search time by improved BOVW with wavelet decomposition

2020 ◽  
Vol 21 (2) ◽  
pp. 1201
Author(s):  
Mohammed El Amin Kourtiche ◽  
Mohammed Beladgham ◽  
Abdelmalik Taleb-Ahmed
Keyword(s):  
Wavelet Decomposition ◽  
Search Time ◽  
Bag Of Visual Words ◽  
Image Search ◽  
Training Time ◽  
Visual Words ◽  
Computational Costs ◽  
Classification Image ◽  
Wavelet Decompositions ◽  
And Training

<p>In the last decade, the bag of visual words (BOVW) has been used widely in image classification, image retrieval and has significantly improved the performance of CBIR system. In this paper we propose a new method to enhance BOVW using features obtained from wavelet decomposition in order to reduce computational costs in vocabulary construction and training time. We apply several level of wavelet decompositions and evaluate their impact on accuracy of the BOVW. We apply our method on MURA-v1.1 dataset and the experiments results confirm the performance of our approach.</p>

Cross-modal variable-length hashing based on hierarchy

2021 ◽  
Vol 25 (3) ◽  
pp. 669-685
Author(s):  
Xiaojun Qi ◽  
Xianhua Zeng ◽  
Shumin Wang ◽  
Yicai Xie ◽  
Liming Xu
Keyword(s):  
Hash Functions ◽  
Variable Length ◽  
Superior Performance ◽  
Hierarchical Architecture ◽  
Training Time ◽  
Computational Costs ◽  
Research Fields ◽  
Efficient Retrieval ◽  
Benchmark Datasets ◽  
And Training

Due to the emergence of the era of big data, cross-modal learning have been applied to many research fields. As an efficient retrieval method, hash learning is widely used frequently in many cross-modal retrieval scenarios. However, most of existing hashing methods use fixed-length hash codes, which increase the computational costs for large-size datasets. Furthermore, learning hash functions is an NP hard problem. To address these problems, we initially propose a novel method named Cross-modal Variable-length Hashing Based on Hierarchy (CVHH), which can learn the hash functions more accurately to improve retrieval performance, and also reduce the computational costs and training time. The main contributions of CVHH are: (1) We propose a variable-length hashing algorithm to improve the algorithm performance; (2) We apply the hierarchical architecture to effectively reduce the computational costs and training time. To validate the effectiveness of CVHH, our extensive experimental results show the superior performance compared with recent state-of-the-art cross-modal methods on three benchmark datasets, WIKI, NUS-WIDE and MIRFlickr.

High Performance Online Image Search with GPUs on Large Image Databases

2013 ◽  
Vol 4 (3) ◽  
pp. 24-41
Author(s):  
Ali Cevahir ◽  
Junji Torii
Keyword(s):  
Search Engine ◽  
High Performance ◽  
Bag Of Visual Words ◽  
Image Search ◽  
Text Search ◽  
Basic Step ◽  
Keypoint Matching ◽  
Visual Words ◽  
One To One ◽  
Different Characteristics

The authors propose an online image search engine based on local image keypoint matching with GPU support. State-of-the-art models are based on bag-of-visual-words, which is an analogy of textual search for visual search. In this work, thanks to the vector computation power of the GPU, the authors utilize real values of keypoint descriptors and realize real-time search at keypoint level. By keeping the identities of each keypoint, closest keypoints are accurately retrieved. Image search has different characteristics than textual search. The authors implement one-to-one keypoint matching, which is more natural for images. The authors utilize GPUs for every basic step. To demonstrate practicality of GPU-extended image search, the authors also present a simple bag-of-visual-words search technique with full-text search engines. The authors explain how to implement one-to-one keypoint matching with text search engine. Proposed methods lead to drastic performance and precision improvement, which is demonstrated on datasets of different sizes.

scholarly journals P38 COVID-19 impact on Surgical Training And Recovery Planning (COVID-STAR) Survey

BJS Open ◽  
2021 ◽  
Vol 5 (Supplement_1) ◽  
Author(s):  
◽  
Joshua Clements
Keyword(s):  
Surgical Training ◽  
Negative Impact ◽  
Healthcare Delivery ◽  
Cross Sectional Study ◽  
Surgical Trainee ◽  
Surgical Specialty ◽  
Cross Sectional ◽  
Training Time ◽  
Surgical Trainees ◽  
And Training

Abstract Background The COVID-19 pandemic has resulted in dynamic changes to healthcare delivery. Surgery as a specialty has been significantly affected and with that the delivery of surgical training. Method This national, collaborative, cross sectional study comprising 13 surgical trainee associations distributed a pan surgical specialty survey on the COVID-19 impact on surgical training over a 4-week period (11th May - 8th June 2020). The survey was voluntary and open to medical students and surgical trainees of all specialties and training grades. All aspects of training were qualitatively assessed. This study was reported according to STROBE guidelines. Results 810 completed responses were analysed. (M401: F 390) with representation from all deaneries and training grades. 41% of respondents (n = 301) were redeployed with 74% (n = 223) redeployed &gt; 4 weeks. Complete loss of training was reported in elective operating (69.5% n = 474), outpatient activity (67.3%, n = 457), Elective endoscopy (69.5% n = 246) with &gt; 50% reduction in training time reported in emergency operating (48%, n = 326) and completion of work-based assessments (WBA) (46%, n = 309). 81% (n = 551) reported course cancellations and departmental and regional teaching programmes were cancelled without rescheduling in 58% and 60% of cases respectively. A perceived lack of Elective operative exposure and completions of WBA’s were the primary reported factor affecting potential training progression. Overall, &gt; 50% of trainees (n = 377) felt they would not meet the competencies required for that training period. Conclusion This study has demonstrated a perceived negative impact on numerous aspects of surgical training affecting all training specialties and grades.

scholarly journals Efficient Searchable Symmetric Encryption Supporting Dynamic Multikeyword Ranked Search

2020 ◽  
Vol 2020 ◽  
pp. 1-16
Author(s):  
Yu Zhang ◽  
Yin Li ◽  
Yifan Wang
Keyword(s):  
Binary Tree ◽  
Search Time ◽  
Symmetric Encryption ◽  
Time Cost ◽  
Computational Costs ◽  
Related Space ◽  
Insertion And Deletion ◽  
Searchable Symmetric Encryption ◽  
Ranked Search ◽  
Binary Tree Structure

Searchable symmetric encryption that supports dynamic multikeyword ranked search (SSE-DMKRS) has been intensively studied during recent years. Such a scheme allows data users to dynamically update documents and retrieve the most wanted documents efficiently. Previous schemes suffer from high computational costs since the time and space complexities of these schemes are linear with the size of the dictionary generated from the dataset. In this paper, by utilizing a shallow neural network model called “Word2vec” together with a balanced binary tree structure, we propose a highly efficient SSE-DMKRS scheme. The “Word2vec” tool can effectively convert the documents and queries into a group of vectors whose dimensions are much smaller than the size of the dictionary. As a result, we can significantly reduce the related space and time cost. Moreover, with the use of the tree-based index, our scheme can achieve a sublinear search time and support dynamic operations like insertion and deletion. Both theoretical and experimental analyses demonstrate that the efficiency of our scheme surpasses any other schemes of the same kind, so that it has a wide application prospect in the real world.

Sign in / Sign up

Export Citation Format

Share Document