Epitome is its miniature but compact summarization of its most textual and shape components of the original image. In addition, epitomic analysis focuses on the probabilities and statistics over the entire image.
Expecting that this new approach would be useful to other vision applications, we tried to construct a probabilistic framework for face detection using the epitomic analysis, and then challenged to compare the performance with that of PCA analysis, which has been used to develop an efficient computational model for face recognition.
• Project description
• Target Project: Epitome
• Target Scenario: Face Detection using Epitomic analysis
Furthermore, we would like to compare this analysis with PCA.
• Participants; Ji Soo Yi, Woo Young Kim.
• Contributions : Overall, we worked together, including reading the epitome paper, testing data to set the appropriate parameters, epitomic modeling, constructing an algorithm for face detection and analyzing the results. And we also divided our jobs as followings:
• Ji Soo Yi: reading another research papers related to image processing, analyzing epitome code for further application, writing a code for face detection, running a bunch of testing programs.
Woo Young Kim: analyzing the algorithm of epitome modeling by implementing epitome program in person, writing a code that compares the epitomic analysis with PCA, preparing presentation slide and writing a final report
Given that the epitome is a novel representation of its much larger original image yet still containing the most constitutive elements in the original image, the ultimate goal of our project is to verify that epitomic representation is useful for many vision applications, such as object recognition or detection, image denoising, image segmentation and motion tracking. Among them, we focused on face detection using epitomic analysis and tried to compare it with Principal Component Analysis (PCA), which is one of template based approaches and has been used to develop an efficient computational model for face recognition, in terms of computational time and performances.
Throughout this experiment, of course, there have been following difficulties.
First, it is needed to choose the right collection of face images in order to extract an appropriate epitome from it. Next, it is found out the epitomic image really depends on the size of epitome, the size of each patch in the original image, and the number of patches. After an appropriate epitome is extracted, building a proper inference algorithm for face detection using epitome was also a challenge. Finally, find out the aspect by which we can compare the epitomic analysis with PCA analysis.