Management and retrieval of cultural heritage multimedia collection using ontology

Rubhasy, Albaar; Paramartha, A.A. Gede Yudhi; Budi, Indra; Hasibuan, Zainal A.

doi:10.1109/icitacee.2014.7065752

Cited by 3 publications

(4 citation statements)

References 7 publications

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“…Object Retrieval Many previous works investigated approaches to image retrieval. Rubhasy et al [15] used an ontology-based approach to retrieval in multimedia cultural heritage collections. The goal is to enable the integration of different types of cultural heritage media and to retrieve relevant heritage media given a query.…”

Section: Related Work Visitors Behavioural Understanding and Site Man...mentioning

confidence: 99%

EGO-CH: Dataset and fundamental tasks for visitors behavioral understanding using egocentric vision

Ragusa

Furnari

Battiato

et al. 2020

Pattern Recognition Letters

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Equipping visitors of a cultural site with a wearable device allows to easily collect information about their preferences which can be exploited to improve the fruition of cultural goods with augmented reality. Moreover, egocentric video can be processed using computer vision and machine learning to enable an automated analysis of visitors' behavior. The inferred information can be used both online to assist the visitor and offline to support the manager of the site. Despite the positive impact such technologies can have in cultural heritage, the topic is currently understudied due to the limited number of public datasets suitable to study the considered problems. To address this issue, in this paper we propose EGOcentric-Cultural Heritage (EGO-CH), the first dataset of egocentric videos for visitors' behavior understanding in cultural sites. The dataset has been collected in two cultural sites and includes more than 27 hours of video acquired by 70 subjects, with labels for 26 environments and over 200 different Points of Interest. A large subset of the dataset, consisting of 60 videos, is associated with surveys filled out by real visitors. To encourage research on the topic, we propose 4 challenging tasks (room-based localization, point of interest/object recognition, object retrieval and survey prediction) useful to understand visitors' behavior and report baseline results on the dataset.

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Section: Related Work Visitors Behavioural Understanding and Site Man...mentioning

confidence: 99%

EGO-CH: Dataset and fundamental tasks for visitors behavioral understanding using egocentric vision

Ragusa

Furnari

Battiato

et al. 2020

Pattern Recognition Letters

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“…In the article [10] proposed an ontology-based approach in managing cultural heritage and retrieval of multimedia collections, (Figure.3) where the objective is to allow the integration of different types of media and retrieve relevant heritage media of various types given query. For that built an ontology based on the cultural heritage of the UNESCO World Heritage Classification / WHC.…”

Section: Ontologymentioning

confidence: 99%

“…Logical View Building produces Object Description (D1,D2,D3, and D4) that represented in text form with metadata. Concept Detector with Learning model using Convolutional Neural Network [33] performs training and testing specific multimedia dataset (Indonesia Cultural Heritage) that [10] will identify and classify object description to become Concept terms. The result of Concept Detector is concept term that is stored in Unified Multimedia Index database and waiting for the matching process with the multimedia query in the searching task process.…”

Section: ) Indexing Taskmentioning

confidence: 99%

“…For experiments need we use specific ontology (cultural heritage from UNESCO) to create a data constraint in this research. [10] In the second part of this paper discusses more related to IR System and MIR System, included Ontology and Concept Detector. While the third part describes the design Unified Concept-Based MIR technique, included the interface design and architectural design that we have proposed with its explanation, and the last, part four is the conclusion.…”

Section: Introductionmentioning

confidence: 99%

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Unified Concept-based Multimedia Information Retrieval Technique

Kambau¹,

Hasibuan²

2017

EECSI

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The explosion of digital data in the last two decades followed by the development of various types of data, including text, images, audio and video known as multimedia data. Multimedia Information Retrieval is required to search various type of media. There is comprehensive information need that can not be handled by the monolithic search engine like Google, Google Image, Youtube, or FindSounds. The shortcoming of search engine today related to their format or media is the dominance of text format, while the expected information could be an image, audio or video. Hence it is necessary to present multimedia format at the same time. This paper tries to design Unified Concept-based Multimedia Information Retrieval (UCpBMIR) technique to tackle those difficulties by using unified multimedia indexing. The indexing technique transforms the various of media with their features into text representation with the concept-based algorithm and put it into the concept detector. Learning model configures the concept detector to classify the multimedia object. The result of the concept detector process is placed in unified multimedia index database and waiting for the concept-based query to be matched into the Semantic Similarities with ontology. The ontology will provide the relationship between object representation of multimedia data. Due to indexing text, image, audio, and video respectively that naturally, they are heterogeneous, but conceptually they may have the relationship among them. From the preliminary result that multimedia document retrieved can be obtained through single query any format in order to retrieve all kind of multimedia format. Unified multimedia indexing technique with ontology will unify each format of multimedia.

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Unified concept-based multimedia information retrieval technique

Kambau

Hasibuan

2017

2017 4th International Conference on Electrical Engineering, Computer Science and Informatics (EECSI)

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Management and retrieval of cultural heritage multimedia collection using ontology

Cited by 3 publications

References 7 publications

EGO-CH: Dataset and fundamental tasks for visitors behavioral understanding using egocentric vision

EGO-CH: Dataset and fundamental tasks for visitors behavioral understanding using egocentric vision

Unified Concept-based Multimedia Information Retrieval Technique

Unified concept-based multimedia information retrieval technique

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