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Article

Lift Charts-Based Binary Classification in Unsupervised Setting for Concept-Based Retrieval of Emotionally Annotated Images from Affective Multimedia Databases

1
Department of Applied Computing, Faculty of Electrical Engineering and Computing, University of Zagreb, Unska 3, HR-10000 Zagreb, Croatia
2
Department of Electronics, Microelectronics, Computer and Intelligent Systems, Faculty of Electrical Engineering and Computing, University of Zagreb, Unska 3, HR-10000 Zagreb, Croatia
3
Department of Computer Science and Information Technology, Zagreb University of Applied Sciences, Vrbik 8, HR-10000 Zagreb, Croatia
*
Author to whom correspondence should be addressed.
Information 2020, 11(9), 429; https://doi.org/10.3390/info11090429
Received: 20 June 2020 / Revised: 31 August 2020 / Accepted: 1 September 2020 / Published: 3 September 2020
(This article belongs to the Section Information Processes)
Evaluation of document classification is straightforward if complete information on the documents’ true categories exists. In this case, the rank of each document can be accurately determined and evaluated. However, in an unsupervised setting, where the exact document category is not available, lift charts become an advantageous method for evaluation of the retrieval quality and categorization of ranked documents. We introduce lift charts as binary classifiers of ranked documents and explain how to apply them to the concept-based retrieval of emotionally annotated images as one of the possible retrieval methods for this application. Furthermore, we describe affective multimedia databases on a representative example of the International Affective Picture System (IAPS) dataset, their applications, advantages, and deficiencies, and explain how lift charts may be used as a helpful method for document retrieval in this domain. Optimization of lift charts for recall and precision is also described. A typical scenario of document retrieval is presented on a set of 800 affective pictures labeled with an unsupervised glossary. In the lift charts-based retrieval using the approximate matching method, the highest attained accuracy, precision, and recall were 51.06%, 47.41%, 95.89%, and 81.83%, 99.70%, 33.56%, when optimized for recall and precision, respectively. View Full-Text
Keywords: image classification; image retrieval; concept based retrieval; affective computing; performance evaluation; lift charts image classification; image retrieval; concept based retrieval; affective computing; performance evaluation; lift charts
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MDPI and ACS Style

Horvat, M.; Jović, A.; Ivošević, D. Lift Charts-Based Binary Classification in Unsupervised Setting for Concept-Based Retrieval of Emotionally Annotated Images from Affective Multimedia Databases. Information 2020, 11, 429. https://doi.org/10.3390/info11090429

AMA Style

Horvat M, Jović A, Ivošević D. Lift Charts-Based Binary Classification in Unsupervised Setting for Concept-Based Retrieval of Emotionally Annotated Images from Affective Multimedia Databases. Information. 2020; 11(9):429. https://doi.org/10.3390/info11090429

Chicago/Turabian Style

Horvat, Marko, Alan Jović, and Danko Ivošević. 2020. "Lift Charts-Based Binary Classification in Unsupervised Setting for Concept-Based Retrieval of Emotionally Annotated Images from Affective Multimedia Databases" Information 11, no. 9: 429. https://doi.org/10.3390/info11090429

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