Exploiting Visual Saliency Algorithms for Object-Based Attention: A New Color and Scale-Based Approach

Ardizzone, Edoardo; Bruno, Alessandro; Gugliuzza, Francesco

doi:10.1007/978-3-319-68548-9_18

Cited by 2 publications

(12 citation statements)

References 28 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The saliency values are the distances between the narrowed images and the means of their channels. We employed interest points such as SIFT keypoints to extract saliency maps and texture scale [37]- [40]. [41] proposed a low-resolution saliency estimate based on random colour sampling.…”

Section: ) Traditional Approaches-based Saliency Detectionmentioning

confidence: 99%

“…In our previous work [40] we used an eye-tracker to record the gaze path of 24 observers while viewing each image from a subset of OPED (Object Pose Estimation Database) [100], [101]. It gave rise to a new dataset provided with fixation point maps we made publicly available under the name of ETTO (Eye Tracking Through Objects) [11].…”

Section: B Eye-tracking Datasetsmentioning

confidence: 99%

“…The overall architecture of our new proposed saliency extraction is based on the extension of our Keypoint Density Maps (KDMs) [37], [39], [40]. Our method projects KDMs onto a more biologically inspired multiscale architecture.…”

Section: A Extraction Of Keypoint Density Mapsmentioning

confidence: 99%

“…In the past, to improve the effectiveness of our algorithm, we added colour information [40] from two colour spaces such as HSV, and CIE L*a*b*. Experimental evidence showed several drawbacks of HSV.…”

Section: A Extraction Of Keypoint Density Mapsmentioning

confidence: 99%

“…KDM algorithm needs a scale factor to work: this parameter is a function of the size of the image [40].…”

Section: A Extraction Of Keypoint Density Mapsmentioning

confidence: 99%

See 4 more Smart Citations

A Multi-Scale Colour and Keypoint Density-Based Approach for Visual Saliency Detection

et al. 2020

Self Cite

View full text Add to dashboard Cite

In the first seconds of observation of an image, several visual attention processes are involved in the identification of the visual targets that pop-out from the scene to our eyes. Saliency is the quality that makes certain regions of an image stand out from the visual field and grab our attention. Saliency detection models, inspired by visual cortex mechanisms, employ both colour and luminance features. Furthermore, both locations of pixels and presence of objects influence the Visual Attention processes. In this paper, we propose a new saliency method based on the combination of the distribution of interest points in the image with multiscale analysis, a centre bias module and a machine learning approach. We use perceptually uniform colour spaces to study how colour impacts on the extraction of saliency. To investigate eye-movements and assess the performances of saliency methods over object-based images, we conduct experimental sessions on our dataset ETTO (Eye Tracking Through Objects). Experiments show our approach to be accurate in the detection of saliency concerning state-of-the-art methods and accessible eye-movement datasets. The performances over object-based images are excellent and consistent on generic pictures. Besides, our work reveals interesting findings on some relationships between saliency and perceptually uniform colour spaces.

show abstract

Section: ) Traditional Approaches-based Saliency Detectionmentioning

confidence: 99%

Section: B Eye-tracking Datasetsmentioning

confidence: 99%

Section: A Extraction Of Keypoint Density Mapsmentioning

confidence: 99%

Section: A Extraction Of Keypoint Density Mapsmentioning

confidence: 99%

“…KDM algorithm needs a scale factor to work: this parameter is a function of the size of the image [40].…”

Section: A Extraction Of Keypoint Density Mapsmentioning

confidence: 99%

See 3 more Smart Citations

A Multi-Scale Colour and Keypoint Density-Based Approach for Visual Saliency Detection

et al. 2020

Self Cite

View full text Add to dashboard Cite

show abstract

Image Content Enhancement Through Salient Regions Segmentation for People With Color Vision Deficiencies

et al. 2019

Self Cite

View full text Add to dashboard Cite

Color vision deficiencies affect visual perception of colors and, more generally, color images. Several sciences such as genetics, biology, medicine, and computer vision are involved in studying and analyzing vision deficiencies. As we know from visual saliency findings, human visual system tends to fix some specific points and regions of the image in the first seconds of observation summing up the most important and meaningful parts of the scene. In this article, we provide some studies about human visual system behavior differences between normal and color vision-deficient visual systems. We eye-tracked the human fixations in first 3 seconds of observation of color images to build real fixation point maps. One of our contributions is to detect the main differences between the aforementioned human visual systems related to color vision deficiencies by analyzing real fixation maps among people with and without color vision deficiencies. Another contribution is to provide a method to enhance color regions of the image by using a detailed color mapping of the segmented salient regions of the given image. The segmentation is performed by using the difference between the original input image and the corresponding color blind altered image. A second eye-tracking of color blind people with the images enhanced by using recoloring of segmented salient regions reveals that the real fixation points are then more coherent (up to 10%) with the normal visual system. The eye-tracking data collected during our experiments are in a publicly available dataset called Eye-Tracking of Color Vision Deficiencies.

show abstract

Exploiting Visual Saliency Algorithms for Object-Based Attention: A New Color and Scale-Based Approach

Cited by 2 publications

References 28 publications

A Multi-Scale Colour and Keypoint Density-Based Approach for Visual Saliency Detection

A Multi-Scale Colour and Keypoint Density-Based Approach for Visual Saliency Detection

Image Content Enhancement Through Salient Regions Segmentation for People With Color Vision Deficiencies

Contact Info

Product

Resources

About