Search Results

Now showing 1 - 3 of 3

Some of the metrics are blocked by your
consent settings
Utilising Kronecker Decomposition and Tensor-based Multi-view Learning to predict where people are looking in images
(2017-07-26)
Pasupa, Kitsuchart
;
Szedmak, Sandor
Eye movement data collection is very expensive and laborious. Moreover, there are usually missing values. Assuming that we are collecting eye movement data from a set of images viewed by different users, there is a possibility that we will not able to collect the data of every user from every image–one or more views may not be represented in the image. We assume that the relationships among the views can be learnt from the whole collection of views (or items). The task is then to reproduce the missing part of the incomplete items from the relationships derived from the complete items and the known part of these items. Using certain properties of tensor algebra, we showed that this problem can be formulated consistently as a regression type learning task. Furthermore, there is a maximum margin based optimisation framework in which this problem can be solved in a tractable way. This problem is similar to learning to predict where a person is looking in an image. Therefore, we proposed an algorithm called “Tensor-based Multi-View Learning”(TMVL) in this paper. Furthermore, we also proposed a technique for improving prediction by introducing a new feature set obtained from Kronecker decomposition of the image fused with user's eye movement data. Using this new feature can improve prediction performance markedly. The proposed approach was proven to be more effective than two well-known saliency detection techniques.
Some of the metrics are blocked by your
consent settings
Using image features and eye tracking device to predict human emotions towards abstract images
(2016-01-01)
Pasupa, Kitsuchart
;
Chatkamjuncharoen, Panawee
;
Wuttilertdeshar, Chotiros
;
Sugimoto, Masanori
Nowadays, emotional semantic image retrieval system enables users to access images that they want in a database according to emotional concept. This leads to affective image classification task which recently attracts researchers’ attention. However, different users may experience different emotions depending on where, in the image, they are gazing on. This paper presents an improved prediction method by taking into account the users eye movement as implicit feedback while they are looking at the image. Our experimental results show that using both eye movement information and image feature together to determine users emotion gave more accurate predictions than using image feature alone.
Some of the metrics are blocked by your
consent settings
Learning to predict where people look with tensor-based multi-view learning
(2015-01-01)
Pasupa, Kitsuchart
;
Szedmak, Sandor
Eye movements data collection is very expensive and laborious. Moreover, there are usually missing values. Assuming that we are collecting eye movements data on a set of images from different users (views). There is a possibility that we are not able to collect eye movements of all users on all images. One or more views are not represented in the image. We assume that the relationships among the views can be learnt from the complete items. The task is then to reproduce the missing part of the incomplete items from the relationships derived from the complete items and the known part of these items. Using the properties of tensor algebra we show that this problem can be formulated consistently as a regression type learning task. Furthermore, there is a maximum margin based optimisation framework where this problem can be solved in a tractable way. This problem is similar to learning to predict where human look. The proposed algorithm is proved to be more effective than well-known saliency detection techniques.

KMITL

Browse

Filters

Settings

Sort By

Results per page

Search Results