Incremental Tag Suggestion for Landmark Image Collections

In recent social media applications, descriptive information is collected through user tagging, such as face recognition, and automatic environment sensing, such as GPS. There are many applications that recognize landmarks using information gathered from GPS data. However, GPS is dependent on the location of the camera, not the landmark. In this research, we propose an automatic landmark tagging scheme using secondary regions to distinguish between similar landmarks. We propose two algorithms: 1) landmark tagging by secondary objects and 2) automatic new landmark recognition. Images of 30 famous landmarks from various public databases were used in our experiment. Results show increments of tagged areas and the improvement of landmark tagging accuracy

Feature Reduction in Graph Analysis

A common approach to improve medical image classification is to add more features to the classifiers; however, this increases the time required for preprocessing raw data and training the classifiers, and the increase in features is not always beneficial. The number of commonly used features in the literature for training of image feature classifiers is over 50. Existing algorithms for selecting a subset of available features for image analysis fail to adequately eliminate redundant features. This paper presents a new selection algorithm based on graph analysis of interactions among features and between features to classifier decision. A modification of path analysis is done by applying regression analysis, multiple logistic and posterior Bayesian inference in order to eliminate features that provide the same contributions. A database of 113 mammograms from the Mammographic Image Analysis Society was used in the experiments. Tested on two classifiers – ANN and logistic regression – cancer detection accuracy (true positive and false-positive rates) using a 13-feature set selected by our algorithm yielded substantially similar accuracy as using a 26-feature set selected by SFS and results using all 50-features. However, the 13-feature greatly reduced the amount of computation needed

MOTION ESTIMATION AND DETECTION OF COMPLEX OBJECT BY ANALYZING RESAMPLED MOVEMENTS OF PARTS

A moving object that has many complex moving parts is very hard to detect and its motion is not easy to estimate. In this paper, we present a new technique for motion estimation and detection of moving complex objects by analyzing the resampled motions of the parts of objects. The Kalman filter is used to track all resampled movements and the tracked routes are classified into groups that share the same fundamental movements. Our simulation show that recall of motion estimation and detection is approximately 0.8, while the computation drops exponentially. 1

An approach for parallelizing the BlastN

BlastN is a well-known and useful tool for sequence alignment. The program works by receiving a DNA sequence query and then comparing the query with all sequences in a DNA database. Because of continuously increase in database size, the response time on a singleprocessor system tends to be longer. This paper presents a new approach for parallelizing the BlastN onto a computer cluster in order to decrease response times without any differences from search results of the original BlastN algorithm. Moreover, the new approach supports more concurrent BlastN users, as well

The Selection of Useful Visual Words for Class-Imbalanced Data in Image Classification

The bag of visual words (BOVW) has recently been used for image classification in large datasets. A major problem of image classification using BOVW is high dimensionality, with most features usually being irrelevant and different BOVW for multi-view images in each class. Therefore, the selection of significant visual words for multi-view images in each class is an essential method to reduce the size of BOVW while retaining the high performance of image classification. Many feature scores for ranking produce low classification performance for class imbalanced distributions and multi-views in each class. We propose a feature score based on the statistical t-test technique, which is a statistical evaluation of the difference between two sample means, to assess the discriminating power of each individual feature. The multi-class image classification performance of the proposed feature score is compared with four modern feature scores, such as Document Frequency (DF), Mutual information (MI), Pointwise Mutual information (PMI) and Chi-square statistics (CHI). The results show that the average F1-measure performance on the Paris dataset and the SUN397 dataset using the proposed feature score are 92% and 94%, respectively, while all other feature scores do not exceed 80%