CASCADE OF MULTI-LEVEL MULTI-INSTANCE CLASSIFIERS FOR IMAGE ANNOTATION

2011 ◽  
Keyword(s):  
Image Annotation ◽  
Multi Level

scholarly journals Automatic Image Annotation by Sequentially Learning From Multi-Level Semantic Neighborhoods

IEEE Access ◽  
2021 ◽  
Vol 9 ◽  
pp. 135742-135754
Author(s):  
Houjie Li ◽  
Wei Li ◽  
Hongda Zhang ◽  
Xin He ◽  
Mingxiao Zheng ◽  
...  
Keyword(s):  
Image Annotation ◽  
Automatic Image Annotation ◽  
Multi Level

Ontology-Based Image Classification and Annotation

2020 ◽  
Vol 34 (11) ◽  
pp. 2040002 ◽  
Author(s):  
Jalila Filali ◽  
Hajer Baazaoui Zghal ◽  
Jean Martinet
Keyword(s):  
Image Classification ◽  
Rapid Growth ◽  
Recent Progress ◽  
Image Annotation ◽  
Bag Of Visual Words ◽  
Challenging Problem ◽  
Visual Words ◽  
Max Pooling ◽  
Multi Level

With the rapid growth of image collections, image classification and annotation has been active areas of research with notable recent progress. Bag-of-Visual-Words (BoVW) model, which relies on building visual vocabulary, has been widely used in this area. Recently, attention has been shifted to the use of advanced architectures which are characterized by multi-level processing. Hierarchical Max-Pooling (HMAX) model has attracted a great deal of attention in image classification. To improve image classification and annotation, several approaches based on ontologies have been proposed. However, image classification and annotation remain a challenging problem due to many related issues like the problem of ambiguity between classes. This problem can affect the quality of both classification and annotation results. In this paper, we propose an ontology-based image classification and annotation approach. Our contributions consist of the following: (1) exploiting ontological relationships between classes during both image classification and annotation processes; (2) combining the outputs of hypernym–hyponym classifiers to lead to a better discrimination between classes; and (3) annotating images by combining hypernym and hyponym classification results in order to improve image annotation and to reduce the ambiguous and inconsistent annotations. The aim is to improve image classification and annotation by using ontologies. Several strategies have been experimented, and the obtained results have shown that our proposal improves image classification and annotation.

A Multi-Label Image Annotation With Multi-Level Tagging System

2018 ◽  
pp. 28-47
Author(s):  
Kalaivani Anbarasan ◽  
Chitrakala S.
Keyword(s):  
Image Retrieval ◽  
Retrieval System ◽  
Image Annotation ◽  
Image Features ◽  
Semantic Gap ◽  
Low Level ◽  
Image Retrieval System ◽  
Multi Level ◽  
High Level ◽  
Tagging System

The content based image retrieval system retrieves relevant images based on image features. The lack of performance in the content based image retrieval system is due to the semantic gap. Image annotation is a solution to bridge the semantic gap between low-level content features and high-level semantic concepts Image annotation is defined as tagging images with a single or multiple keywords based on low-level image features. The major issue in building an effective annotation framework is the integration of both low level visual features and high-level textual information into an annotation model. This chapter focus on new statistical-based image annotation model towards semantic based image retrieval system. A multi-label image annotation with multi-level tagging system is introduced to annotate image regions with class labels and extract color, location and topological tags of segmented image regions. The proposed method produced encouraging results and the experimental results outperformed state-of-the-art methods

Verlaufsanalyse wöchentlicher Selbst- und Fremdeinschätzungen in der Langzeittherapie suchtkranker Jugendlicher

2019 ◽  
Vol 47 (2) ◽  
pp. 126-137
Author(s):  
Ferdinand Keller ◽  
Tatjana Stadnitski ◽  
Jakob Nützel ◽  
Renate Schepker
Keyword(s):  
Allen Vier ◽  
Multi Level

Zusammenfassung. Fragestellung: Über Veränderungen in der emotionalen Befindlichkeit von Jugendlichen während einer Suchttherapie ist wenig bekannt. Methode: Die Jugendlichen füllten wöchentlich einen entsprechenden Fragebogen aus, analog ihre Bezugsbetreuer eine parallelisierte Kurzfassung. Von 42 Jugendlichen liegen insgesamt 853 Bogen und von den Bezugsbetreuern 708 Bogen vor. Die Fragebogen wurden zunächst faktorenanalytisch hinsichtlich ihrer Dimensionalität ausgewertet, anschließend wurden gruppenbezogene Verlaufsanalysen (Multi-Level-Modelle) und Abhängigkeitsanalysen auf Einzelfallebene (Zeitreihenanalysen) durchgeführt. Ergebnisse: Im Jugendlichenfragebogen ergaben sich vier Faktoren: negative Befindlichkeit, Wertschätzung von Therapie/Betreuung, Motivation und Suchtdynamik. Die Übereinstimmung zwischen den Jugendlichen- und der (einfaktoriellen) Betreuereinschätzung fiel insgesamt niedrig bis mäßig aus, brachte aber auf Einzelfallebene differenziertere Ergebnisse. Im Verlauf nahmen die Werte auf allen vier Jugendlichenskalen ab. Einzig der Verlauf der Wertschätzung in der Eingewöhnungsphase war prädiktiv für den späteren Abbruch der Maßnahme: Bei den Abbrechern nahm die Wertschätzung ab, während sie bei den Beendern initial stieg. Schlussfolgerungen: Der bedeutsamste Faktor in Bezug auf die Therapiebeendigung suchtkranker Jugendlicher scheint die Wertschätzung von Therapie/Betreuung zu sein, während die Motivation jugendtypische Schwankungen aufweist. Der Suchtdynamik kam eine deutlich weniger bedeutende Rolle zu als allgemein angenommen. Programme in der Langzeittherapie sollten die Wertschätzung von Therapie/Betreuung künftig mehr fokussieren als die Suchtdynamik.

A Multi-level Measure Of Safety Climate And Associations With Safety Behaviors In The Fire Service

2017 ◽  
Author(s):  
Todd D. Smith ◽  
Mari-Amanda Dyal ◽  
Yongjia Pu ◽  
Stephanie Dickinson ◽  
David M. DeJoy
Keyword(s):  
Safety Climate ◽  
Fire Service ◽  
Safety Behaviors ◽  
Multi Level
Sign in / Sign up

Export Citation Format

Share Document