HyText – A Scene-Text Extraction Method for Video Retrieval

Theus, Alexander; Rossetto, Luca; Bernstein, Abraham

doi:10.1007/978-3-030-98355-0_16

Cited by 5 publications

(2 citation statements)

References 21 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Text Retrieval: We use the text retrieval functionality of Cottontail DB and Cineast to query the provided textual descriptions and OCR data. We use both the provided OCR data as well as our own HyText [37]. Visual Similarity Search: vitrivr offers several features for content-based image retrieval, such as one based on Incep-tionResnetV2 [36].…”

Section: Existing Functionality For Lifelog Retrievalmentioning

confidence: 99%

vitrivr at the Lifelog Search Challenge 2022

Heller

Rossetto

Sauter

et al. 2022

Proceedings of the 5th Annual on Lifelog Search Challenge

Self Cite

View full text Add to dashboard Cite

show abstract

Section: Existing Functionality For Lifelog Retrievalmentioning

confidence: 99%

vitrivr at the Lifelog Search Challenge 2022

Heller

Rossetto

Sauter

et al. 2022

Proceedings of the 5th Annual on Lifelog Search Challenge

Self Cite

View full text Add to dashboard Cite

show abstract

“…The extraction and analysis of text from images and videos have emerged as focal points within multimedia understanding systems. Numerous methods have been put forward for extracting text from visual media, and many studies have offered comprehensive reviews [10][11][12][13] on this subject. Text recognition and extraction address two primary challenges when locating text within a target image.…”

Section: Introductionmentioning

confidence: 99%

Intelligent Image Text Detection via Pixel Standard Deviation Representation

Guia,

Laouid,

Hammoudeh

et al. 2024

CSSE

View full text Add to dashboard Cite

Artificial intelligence has been involved in several domains. Despite the advantages of using artificial intelligence techniques, some crucial limitations prevent them from being implemented in specific domains and locations. The accuracy, poor quality of gathered data, and processing time are considered major concerns in implementing machine learning techniques, certainly in low-end smart devices. This paper aims to introduce a novel pretreatment technique dedicated to image text detection that uses the images' pixel divergence and similarity to reduce the image size. Mitigating the image size while keeping its features improves the model training time with an acceptable accuracy rate. The mitigation is reached by gathering similar image pixels in one pixel based on calculated values of the standard deviation σ , where we consider that two pixels are similar if they have approximately the same σ values. The work proposes a new pipeline approach that reduces the size of the image in the input and intermediate layers of a deep learning model based on merged pixels using standard deviation values instead of the whole image. The experimental results prove that this technique significantly improves the performance of existing text detection methods, particularly in challenging scenarios such as using low-end IoT devices that offer low contrast or noisy backgrounds. Compared with other techniques, the proposed technique can potentially be exploited for text detection in IoT-gathered multimedia data with reasonable accuracy in a short computation time. Evaluation of the MSRA-TD500 dataset demonstrates the remarkable performance of our approach, Standard Deviation Network (σ Net), with precision and recall values of 93.8% and 85.6%, respectively, that outperform recent research results.

show abstract

A tale of two interfaces: vitrivr at the lifelog search challenge

Heller

Spieß

Schuldt

2023

Multimed Tools Appl

View full text Add to dashboard Cite

The past decades have seen an exponential growth in the amount of data which is produced by individuals. Smartphones which capture images, videos and sensor data have become commonplace, and wearables for fitness and health are growing in popularity. Lifelog retrieval systems aim to aid users in finding and exploring their personal history. We present two systems for lifelog retrieval: vitrivr and vitrivr-VR, which share a common retrieval model and backend for multi-modal multimedia retrieval. They differ in the user interface component, where vitrivr relies on a traditional desktop-based user interface and vitrivr-VR has a Virtual Reality user interface. Their effectiveness is evaluated at the Lifelog Search Challenge 2021, which offers an opportunity for interactive retrieval systems to compete with a focus on textual descriptions of past events. Our results show that the conventional user interface outperformed the VR user interface. However, the format of the evaluation campaign does not provide enough data for a thorough assessment and thus to make robust statements about the difference between the systems. Thus, we conclude by making suggestions for future interactive evaluation campaigns which would enable further insights.

show abstract

HyText – A Scene-Text Extraction Method for Video Retrieval

Cited by 5 publications

References 21 publications

vitrivr at the Lifelog Search Challenge 2022

vitrivr at the Lifelog Search Challenge 2022

Intelligent Image Text Detection via Pixel Standard Deviation Representation

A tale of two interfaces: vitrivr at the lifelog search challenge

Contact Info

Product

Resources

About