Bridging semantic gap between high-level and low-level features in content-based video retrieval using multi-stage ESN–SVM classifier

Brindha, N.; Visalakshi, P.

doi:10.1007/s12046-016-0574-8

Cited by 9 publications

(3 citation statements)

References 5 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Social and semantic data have been used in other areas of multimedia data such as video retrieval [21] video summarization [22]. With the recent increase in the volume of available digital photos, a wider range of applications beyond their traditional ones came to benefit from Image collection summarization; such as life logging systems [23] and Object tracking [24] in this paper, we propose a multicriteria context-sensitive approach for image collection summarization applying semantic and attractiveness data.…”

Section: Related Workmentioning

confidence: 99%

A multi-criteria context-sensitive approach for social image collection summarization

Samani

Moghaddam

2018

Sādhanā

View full text Add to dashboard Cite

Recent increase in the number of digital photos in the content sharing and social networking websites has created an endless demand for techniques to analyze, navigate, and summarize these images. In this paper, we focus on image collection summarization. Earlier methods in image collection summarization consider representativeness and diversity criteria while recent ones also consider other criteria such as image quality, aesthetic or appeal. In this paper, we propose a multi-criteria context-sensitive approach for social image collection summarization. In the proposed method, two different sets of features are combined while each one looks at different criteria for image collection summarization: social attractiveness features and semantic features. The first feature set considers different aspects that make an image appealing such as image quality, aesthetic, and emotion to create attractiveness score for input images while the second one covers semantic content of images and assigns semantic score to them. We use social network infrastructure to identify attractiveness features and domain ontology for extracting ontology features. The final summarization is provided by integrating the attractiveness and semantic features of input images. The experimental results on a collection of human generated summaries on a set of Flickr images demonstrate the effectiveness of the proposed image collection summarization approach.

show abstract

Section: Related Workmentioning

confidence: 99%

A multi-criteria context-sensitive approach for social image collection summarization

Samani

Moghaddam

2018

Sādhanā

View full text Add to dashboard Cite

show abstract

“…Numerous studies have been conducted in the area of audio recognition over the past few decades [5][6][7][8][9]. Most of the traditional approaches require handcrafted features to provide metadata.…”

Section: Introductionmentioning

confidence: 99%

Audio Interval Retrieval Using Convolutional Neural Networks

Kuzminykh¹,

Shevchuk²,

Shiaeles³

et al. 2020

Lecture Notes in Computer Science

View full text Add to dashboard Cite

Modern streaming services are increasingly labeling videos based on their visual or audio content. This typically augments the use of technologies such as AI and ML by allowing to use natural speech for searching by keywords and video descriptions. Prior research has successfully provided a number of solutions for speech to text, in the case of a human speech, but this article aims to investigate possible solutions to retrieve sound events based on a natural language query, and estimate how effective and accurate they are. In this study, we specifically focus on the YamNet, AlexNet, and ResNet-50 pre-trained models to automatically classify audio samples using their respective melspectrograms into a number of predefined classes. The predefined classes can represent sounds associated with actions within a video fragment. Two tests are conducted to evaluate the performance of the models on two separate problems: audio classification and intervals retrieval based on a natural language query. Results show that the benchmarked models are comparable in terms of performance, with YamNet slightly outperforming the other two models. YamNet was able to classify single fixed-size audio samples with 92.7% accuracy and 68.75% precision while its average accuracy on intervals retrieval was 71.62% and precision was 41.95%. The investigated method may be embedded into an automated event marking architecture for streaming services.

show abstract

“…Considering both low-level and semantic features, Brindha and Visalakshi (2017) constructed a multimedia event detection system to bridge the semantic gap in video retrieval.…”

Section: Video Information Retrieval Approaches and Interfacesmentioning

confidence: 99%

Design and evaluation of a video retrieval environment for exploratory searches in a learning context

Loke¹

View full text Add to dashboard Cite

Bridging semantic gap between high-level and low-level features in content-based video retrieval using multi-stage ESN–SVM classifier

Cited by 9 publications

References 5 publications

A multi-criteria context-sensitive approach for social image collection summarization

A multi-criteria context-sensitive approach for social image collection summarization

Audio Interval Retrieval Using Convolutional Neural Networks

Design and evaluation of a video retrieval environment for exploratory searches in a learning context

Contact Info

Product

Resources

About