Motofumi Fukui

Bag-of-features approach for improvement of lung tissue classification in diffuse lung disease

Many automated techniques have been proposed to classify diffuse lung disease patterns. Most of the techniques utilize texture analysis approaches with second and higher order statistics, and show successful classification result among various lung tissue patterns. However, the approaches do not work well for the patterns with inhomogeneous texture distribution within a region of interest (ROI), such as reticular and honeycombing patterns, because the statistics can only capture averaged feature over the ROI. In this work, we have introduced the bag-of-features approach to overcome this difficulty. In the approach, texture images are represented as histograms or distributions of a few basic primitives, which are obtained by clustering local image features. The intensity descriptor and the Scale Invariant Feature Transformation (SIFT) descriptor are utilized to extract the local features, which have significant discriminatory power due to their specificity to a particular image class. In contrast, the drawback of the local features is lack of invariance under translation and rotation. We improved the invariance by sampling many local regions so that the distribution of the local features is unchanged. We evaluated the performance of our system in the classification task with 5 image classes (ground glass, reticular, honeycombing, emphysema, and normal) using 1109 ROIs from 211 patients. Our system achieved high classification accuracy of 92.8%, which is superior to that of the conventional system with the gray level co-occurrence matrix (GLCM) feature especially for inhomogeneous texture patterns.

Size-Independent Image Segmentation by Hierarchical Clustering and Its Application for Face Detection

In this paper, we introduce a technique to detect a target object quickly. Our idea is based on onservation on the clusters into which an image is divided by hierarchical k-means clustering with space feature and color feature. This clustering method has the advantage of extracting the region of an object with some varied size. We insist that our idea should lead to detect a target object quickly, because it is not necessary to search the locations containing no targets. First, we evaluate our clustering method and second, we demonstrate that our method is effective on an object detection by applying to our face detection system. We show that the detection time can be reduced by 24%.

Face recognition using surface features in XYI space

We propose a face recognition algorithm that utilizes novel surface features in (x, y, I(x,y)) space. A face image is considered as a surface in XYI space, and the surface is segmented into a definite number of regions by using a Gaussian mixture model. Parameters of each Gaussian distribution are determined by maximizing the log-likelihood function, and are stored as features of each individual face image. In the recognition process, the log-likelihood is used as a similarity measure between a test image and the stored features. The face recognition performance of our algorithm is evaluated with the FERET database. Our algorithm achieves an identification rate of 95.4% and equal error rate of 1.4%, which are superior to other algorithms based on eigenface features and Gabor wavelet features.