Representing an Object by Interchanging What with Where

Exploring representations is a fundamental step towards understanding vision. The visual system carries two types of information along separate pathways: One is about what it is and the other is about where it is. Initially, the what is represented by a pattern of activity that is distributed across millions of photoreceptors, whereas the where is 'implicitly' given as their retinotopic positions. Many computational theories of object recognition rely on such pixel-based representations, but they are insufficient to learn spatial information such as position and size due to the implicit encoding of the where information. 
Here we try transforming a retinal image of an object into its internal image via interchanging the what with the where, which means that patterns of intensity in internal image describe the spatial information rather than the object information. To be concrete, the retinal image of an object is deformed and turned over into a negative image, in which light areas appear dark and vice versa, and the object's spatial information is quantified with levels of intensity on borders of that image. 
Interestingly, the inner part excluding the borders of the internal image shows the position and scale invariance. In order to further understand how the internal image associates the what and where, we examined the internal image of a face which moves or is scaled on the retina. As a result, we found that the internal images form a linear vector space under the object translation and scaling. 
In conclusion, these results show that the what-where interchangeability might play an important role for organizing those two into internal representation of brain

Automatic Image Tagging Model Based on Multigrid Image Segmentation and Object Recognition

Since rapid growth of Internet technologies and mobile devices, multimedia data such as images and videos are explosively growing on the Internet. Managing large scale multimedia data with correct tags and annotations is very important task. Incorrect tags and annotations make it hard to manage multimedia data. Accurate tags and annotation ease management of multimedia data and give high quality retrieve results. Fully manual image tagging which is tagged by user will be most accurate tags when the user tags correct information. Nevertheless, most of users do not make effort on task of tagging. Therefore, we suffer from lots of noisy tags. Best solution for accurate image tagging is to tag image automatically. Robust automatic image tagging models are proposed by many researchers and it is still most interesting research field these days. Since there are still lots of limitations in automatic image tagging models, we propose efficient automatic image tagging model using multigrid based image segmentation and feature extraction method. Our model can improve the object descriptions of images and image regions. Our method is tested with Corel dataset and the result showed that our model performance is efficient and effective compared to other models

Multi-unit Iris Recognition System by Image Check Algorithm

Abstract. In this paper, we propose the iris recognition system, which can select the good quality data between left and right eye images of same person. Although iris recognition system has achieved good performance, but it is affected by the quality of input images. So, eye image check algorithm, which can select the good quality image is very important. The proposed system is composed of four steps. At the first step, both eye images are captured at the same time. At the second step, the eye image check algorithm picks out noisy and counterfeit data between both eye images and offer a good qualified image to the next step. At the third step, Daubechies ’ Wavelet is used as a feature extraction method. Finally, Support Vector Machines(SVM) and Euclidian distance are used as classification methods. Experiment results involve 1694 eye images of 111 different people and the best accuracy rate of 99.1%.

On the Individuality of the Iris Biometric

We consider quantitatively establishing the discriminative power of iris biometric data. It is difficult, however, to establish that any biometric modality is capable of distinguishing every person because the classification task has an extremely large and unspecified number of classes. Here, we propose a methodology to establish a measure of discrimination that is statistically inferable. To establish the inherent distinctness of the classes, i.e., to validate individuality, we transform the many class problem into a dichotomy by using a distance measure between two samples of the same class and between those of two different classes. Various features, distance measures, and classifiers are evaluated. For feature extraction we compare simple binary and multilevel 2D wavelet features. For distance measures we examine scalar distances, feature vector distances, and histogram distances. Finally, for the classifiers we compare Bayes decision rule, nearest neighbor, artificial neural network, and support vector machines. Of the eleven different combinations tested, the best one uses multi-level 2D wavelet features, the his-togram distance, and a support vector machine classifier. Keywords: Biometric individuality, Dichotomy, Iris. 1