Learning top down scene context for visual attention modeling in natural images

Karthikeyan, S.; Jagadeesh, Vignesh; Manjunath, B.S.

doi:10.1109/icip.2013.6738044

Cited by 9 publications

(6 citation statements)

References 23 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Moreover, investing in this project within saliency detection would be a good opportunity to merge some of the Group’s research on both low-level segmentation and high-level face detection. The idea to combine high-level face detection with low-level saliency detection has already been proposed in image-processing papers ( Borji, 2012 ; Karthikeyan et al, 2013 ). But the Group’s ambition here is to go further in the saliency direction as framed by Wang and Li (2008) , after Liu et al (2007) , by proposing an algorithm capable of detecting and segmenting the contours of faces.…”

Section: Reformulating the Saliency Problemmentioning

confidence: 99%

We get the algorithms of our ground truths: Designing referential databases in digital image processing

Jaton

2017

Soc Stud Sci

View full text Add to dashboard Cite

This article documents the practical efforts of a group of scientists designing an image-processing algorithm for saliency detection. By following the actors of this computer science project, the article shows that the problems often considered to be the starting points of computational models are in fact provisional results of time-consuming, collective and highly material processes that engage habits, desires, skills and values. In the project being studied, problematization processes lead to the constitution of referential databases called ‘ground truths’ that enable both the effective shaping of algorithms and the evaluation of their performances. Working as important common touchstones for research communities in image processing, the ground truths are inherited from prior problematization processes and may be imparted to subsequent ones. The ethnographic results of this study suggest two complementary analytical perspectives on algorithms: (1) an ‘axiomatic’ perspective that understands algorithms as sets of instructions designed to solve given problems computationally in the best possible way, and (2) a ‘problem-oriented’ perspective that understands algorithms as sets of instructions designed to computationally retrieve outputs designed and designated during specific problematization processes. If the axiomatic perspective on algorithms puts the emphasis on the numerical transformations of inputs into outputs, the problem-oriented perspective puts the emphasis on the definition of both inputs and outputs.

show abstract

Section: Reformulating the Saliency Problemmentioning

confidence: 99%

We get the algorithms of our ground truths: Designing referential databases in digital image processing

Jaton

2017

Soc Stud Sci

View full text Add to dashboard Cite

show abstract

“…Note that the ground truth in our dataset has multi-level values. For machine-learningbased methods, such as Judd et al, 15 Borji et al 32 and SC, 33 we train a new model by using our dataset and adopt linear regression with 10-fold cross-validation.…”

Section: Resultsmentioning

confidence: 99%

“…8c. We also replace the face-detection feature, used in SC 33 and Borji et al, 32 with our face-importance map, and we train the model again using regression and a 10-fold cross-validation. As illustrated in Fig.…”

Section: The Effectiveness Of the Face-importance Mapmentioning

confidence: 99%

Modeling the importance of faces in natural images

Jin

Yildirim

Lau

et al. 2015

Human Vision and Electronic Imaging XX

View full text Add to dashboard Cite

In this work we study the varying importance of faces in images. Face importance is found to be affected by the size and number of faces present. We collected a dataset of 152 face images with faces in different size and number of faces. We conducted a crowdsourcing experiment where we asked people to label the important regions of the images. Analyzing the results from the experiment, we propose a simple face-importance model, which is a 2D Gaussian function, to quantitatively represent the influence of the size and number of faces on the perceived importance of faces. The face-importance model is then tested for the application of salient-object detection. For this application, we create a new salient-objects dataset, consisting of both face images and non-face images, and also through crowdsourcing we collect the ground truth. We demonstrate that our face-importance model helps us to better locate the important, thus salient, objects in the images and outperforms state-of-the-art salient-object detection algorithms.

show abstract

“…Human inspired visual attention modeling [21,17,14,22,6,7,8,24] has been a well-researched topic in over a decade. Recently there has been significant interest [30,36,43,33,42,29,25,31,44,45,46,38] in eye tracking enhanced computer vision.…”

Section: Related Workmentioning

confidence: 99%