Visual saliency estimation using constraints

Jian, Meng; Wu, Lifang; Jung, Cheolkon; Fu, Qingtao; Jia, Ting

doi:10.1016/j.neucom.2018.02.004

Cited by 5 publications

(5 citation statements)

References 35 publications

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“…The Visual-Saliency game requires players to have the ability to rapidly detect and respond to stimuli within the surrounding environment and to effectively reason visual information (Jian et al., 2018). For example, Steinkuehler and Duncan (2008) found that players often engage in practices such as scientific argumentation and model-based reasoning in order to develop and justify arguments about the game model and about reasoning strategies for playing.…”

Section: Hypothesesmentioning

confidence: 99%

Explorative and Exploitative Learning Affected by Extraneous Cognitive Load and Gameplay Anxiety in a Gestalt Perception Game

Hong¹,

Hwang²,

Chen

et al. 2020

Journal of Educational Computing Research

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In line with the attention-to-affect model, this study employed a game that encompasses Gestalt perception to explore how extraneous cognitive load (ECL) and gameplay anxiety correlate with attitude towards exploitative learning and attitude towards explorative learning as students play the Visual-Saliency game (VSG) with images of artworks. The data of 56 sixth-grade students were collected and subjected to Smart PLS analysis to verify the pathways. The results of this study show that ECL is negatively related to attitude towards exploitative learning and attitude towards explorative learning. However, gameplay anxiety is negatively related to attitude towards exploitative learning, but is not significantly related to explorative learning when playing the VSG. The implication of this study is that Gestalt perception in game playing can give students more opportunities to practice reasoning, and they can gain more knowledge about the artworks themselves.

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Section: Hypothesesmentioning

confidence: 99%

Explorative and Exploitative Learning Affected by Extraneous Cognitive Load and Gameplay Anxiety in a Gestalt Perception Game

Hong¹,

Hwang²,

Chen

et al. 2020

Journal of Educational Computing Research

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“…In top-down models, high-level signs, such as context, semantic, and knowledge information, are used to compute a saliency map. However, bottom-up models are designed based on low-level local features, such as colour, contrast, texture, intensity, motion, and orientation [21,22].…”

Section: A Feature Extraction 1 Visual Saliency Map Generationmentioning

confidence: 99%

“…The regions of an image that attract the greatest attention from the human visual system or have a particular sign of attraction for a human observer are called salient regions. In the literature, great efforts have been dedicated to generate the saliency maps of images without means of eye-trackers and human involvement [20,21]. Saliency detection has been considered for different applications, such as image segmentation, object detection, object recognition, and content-based image retrieval (CBIR) [21].…”

Section: Introductionmentioning

confidence: 99%

“…In the literature, great efforts have been dedicated to generate the saliency maps of images without means of eye-trackers and human involvement [20,21]. Saliency detection has been considered for different applications, such as image segmentation, object detection, object recognition, and content-based image retrieval (CBIR) [21]. As saliency regions attract superior attention through the visual system, they can provide valuable features for retrieval tasks.…”

Section: Introductionmentioning

confidence: 99%

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Document Image Retrieval Based on Visual Saliency Maps

Alaei

Pal

et al. 2019

2019 International Conference on Document Analysis and Recognition Workshops (ICDARW)

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There has been a massive growth in the production of various unstructured, complex and multilingual digitised documents in recent years. Storing and manipulating such digitised documents towards a paperless society has been the objective of emerging technology. As the human visual system can easily distinguish the global summary of images, extracting features based on human attention from images is desirable to achieve more accurate document image retrieval results. Thus, in this research work, an appearance-based document image retrieval system using image saliency maps depending on human visual attention is proposed. The saliency map obtained from the input document image is used to generate a weighted document image. Features are then extracted from the weighted document images using the Gist operator. Then, locality-sensitive hashing is considered to compute similarity distances between a query and the document images in the knowledge-based database. To evaluate the performance of the proposed document image retrieval system MTDB, ITESOFT, and CLEF-IP datasets of document images were used for experimentation. The proposed document image retrieval system provided promising retrieval results compared to the results reported in the literature.

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“…Por su parte, Fang et al (2017) propusieron un método para el aprendizaje de la prominencia visual en imágenes estereoscópicas, que toma en cuenta la información de la profundidad. Jian et al (2018) desarrollaron un sistema para generar mapas de prominencia visual mediante segmentación basada en superpíxeles. Murray et al (2013) intentaron introducir conocimiento a priori en el proceso de prominencia adaptando el modelo de inducción de color de bajo nivel antes de predecir la prominencia.…”

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Fusión temprana de descriptores extraídos de mapas de prominencia multi-nivel para clasificar imágenes

Fidalgo

Alegre

Fernández-Robles

et al. 2019

Rev. iberoam. autom. inform. ind.

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En este artículo proponemos un método que permite mejorar la clasificación de imágenes en conjuntos de datos en los que la imagen contiene un único objeto. Para ello, consideramos los mapas de prominencia como si se trataran de mapas topográficos y filtramos las características del fondo de la imagen mejorando de esta forma la codificación que realiza sobre la imagen completa un modelo clásico basado en Bag of Visual Words (BoVW). En primer lugar, evaluamos seis conocidos algoritmos para la generación de mapas de prominencia y seleccionamos los métodos de GBVS y SIM al determinar que son los que retienen la mayor parte de la información del objeto. Utilizando la información de dichos mapas de prominencia eliminamos los descriptores SIFT extraídos de forma densa pertenecientes al fondo mediante el filtrado de características en base a imágenes binarias obtenidas a diversos niveles del mapa de prominencia. Realizamos el filtrado de descriptores obteniendo capas a diversos niveles del mapa de prominencia, y evaluamos la fusión temprana de los descriptores SIFT contenidos en dichas capas en cinco conjuntos de datos diferentes. Los resultados obtenidos en nuestra experimentación indican que el método propuesto mejora siempre al método de referencia cuando se combinan las dos primeras capas de GBVS o de SIM y el dataset contiene imágenes con un único objeto.

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Visual saliency estimation using constraints

Cited by 5 publications

References 35 publications

Explorative and Exploitative Learning Affected by Extraneous Cognitive Load and Gameplay Anxiety in a Gestalt Perception Game

Explorative and Exploitative Learning Affected by Extraneous Cognitive Load and Gameplay Anxiety in a Gestalt Perception Game

Document Image Retrieval Based on Visual Saliency Maps

Fusión temprana de descriptores extraídos de mapas de prominencia multi-nivel para clasificar imágenes

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