VERGE: A Multimodal Interactive Search Engine for Video Browsing and Retrieval

Moumtzidou, Anastasia; Mironidis, Theodoros; Apostolidis, Evlampios; Markatopoulou, Foteini; Ioannidou, Anastasia; Gialampoukidis, Ilias; Avgerinakis, Konstantinos; Vrochidis, Stefanos; Mezaris, Vasileios; Kompatsiaris, Ioannis; Patras, Ioannis

doi:10.1007/978-3-319-27674-8_39

Cited by 5 publications

(4 citation statements)

References 12 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…3), and supports the submission of queries and the accumulation of relevant retrieval results. The system integrates a fusion module that is able to retrieve multimodal documents and supports (independently) text-based 1 http://mklab-services.iti.gr/verge/ search and image retrieval, using high-level concepts. The aforementioned capabilities allow the user to search through a collection of multimodal documents.…”

Section: Multimedia Retrieval Systemmentioning

confidence: 99%

“…multimedia retrieval, image retrieval, search by visual or textual concept, etc.) already presented in [1], but in a unified user interface.…”

Section: Multimedia Retrieval Systemmentioning

confidence: 99%

“…This paper describes the VERGE interactive multimedia search engine, which is capable of retrieving and browsing multimedia collections. Contrary to the previous versions of VERGE [1], which perform video retrieval using video shots, the present system fuses textual metadata and visual information. The engine integrates a multimodal fusion technique that considers high-level textual and both high-and low-level visual information.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

A multimedia interactive search engine based on graph-based and non-linear multimodal fusion

Moumtzidou

Gialampoukidis

Mironidis

et al. 2016

2016 14th International Workshop on Content-Based Multimedia Indexing (CBMI)

Self Cite

View full text Add to dashboard Cite

This paper presents an interactive multimedia search engine, which is capable of searching into multimedia collections by fusing textual and visual information. Apart from multimedia search, the engine is able to perform text search and image retrieval independently using both high-level and lowlevel information. The images of the multimedia collection are organized by color, offering fast browsing in the image collection.

show abstract

Section: Multimedia Retrieval Systemmentioning

confidence: 99%

“…multimedia retrieval, image retrieval, search by visual or textual concept, etc.) already presented in [1], but in a unified user interface.…”

Section: Multimedia Retrieval Systemmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

A multimedia interactive search engine based on graph-based and non-linear multimodal fusion

Moumtzidou

Gialampoukidis

Mironidis

et al. 2016

2016 14th International Workshop on Content-Based Multimedia Indexing (CBMI)

Self Cite

View full text Add to dashboard Cite

show abstract

“…It is illustrated that visual interactive labeling is a key issue in visual interactive learning, which is always used in machine learning methods such as classfication [4], retrieval [11], and clustering [12]. In recent years, many related machine learning methods have been proposed.…”

Section: Introductionmentioning

confidence: 99%

Perceptual Visual Interactive Learning

Liu¹,

Liu²,

Liu³

et al. 2018

Preprint

View full text Add to dashboard Cite

Supervised learning methods are widely used in machine learning. However, the lack of labels in existing data limits the application of these technologies. Visual interactive learning (VIL) compared with computers can avoid semantic gap, and solve the labeling problem of small label quantity (SLQ) samples in a groundbreaking way. In order to fully understand the importance of VIL to the interaction process, we re-summarize the interactive learning related algorithms (e.g. clustering, classification, retrieval etc.) from the perspective of VIL. Note that, perception and cognition are two main visual processes of VIL. On this basis, we propose a perceptual visual interactive learning (PVIL) framework, which adopts gestalt principle to design interaction strategy and multi-dimensionality reduction (MDR) to optimize the process of visualization. The advantage of PVIL framework is that it combines computer's sensitivity of detailed features and human's overall understanding of global tasks. Experimental results validate that the framework is superior to traditional computer labeling methods (such as label propagation) in both accuracy and efficiency, which achieves significant classification results on dense distribution and sparse classes dataset.

show abstract

Concept-Based Interactive Search System

Nguyen

Zhang

et al. 2016

MultiMedia Modeling

View full text Add to dashboard Cite

VERGE: A Multimodal Interactive Search Engine for Video Browsing and Retrieval

Cited by 5 publications

References 12 publications

A multimedia interactive search engine based on graph-based and non-linear multimodal fusion

A multimedia interactive search engine based on graph-based and non-linear multimodal fusion

Perceptual Visual Interactive Learning

Concept-Based Interactive Search System

Contact Info

Product

Resources

About