Visual Task Performance Assessment using Complementary and Redundant Information within Fused Imagery

Image fusion is the process of combining information from a set of source images to obtain a single image with more relevant information than any individual source image. The intent of image fusion is to produce a single image that renders a better description of the scene than any of the individual source images. Information within source images can be classified as either redundant or complementary. The relevant amounts of complementary and redundant information within the source images provide an effective metric for quantifying the benefits of image fusion. Two common reasons for using image fusion for a particular task are to increase task reliability or to increase capability. It seems natural to associate reliability with redundancy of information between source bands, whereas increased capability is associated with complementary information between source bands. The basic idea is that the more redundant the information between the source images being fused, the less likely an increase in task performance can be realized using the fused imagery. Intuitively, the benefits of image fusion with regards to task performance are maximized when the source images contain large amounts of complementary information. This research introduces a new performance measure based on mutual information which, under the assumption the fused imagery has been properly prepared for human perception, can be used as a predictor of human task performance using the complementary and redundant information in fused imagery. The ability of human observers to identify targets of interest using fused imagery is evaluated using human perception experiments. In the perception experiments, imagery of the same scenes containing targets of interest, captured in different spectral bands, is fused using various fusion algortihms and shown to human observers for identification. The results of the experiments show a correlation exists between the proposed measure and human visual identification task performance. The perception experiments serve to validate the performance prediction accuracy of the new performance measure. the development of the proposed metric introduces into the image fusion community a new image fusion evaluation measure that has the potential to fill many voids within the image fusion literature

Optimal Clustering Framework for Hyperspectral Band Selection

Band selection, by choosing a set of representative bands in hyperspectral image (HSI), is an effective method to reduce the redundant information without compromising the original contents. Recently, various unsupervised band selection methods have been proposed, but most of them are based on approximation algorithms which can only obtain suboptimal solutions toward a specific objective function. This paper focuses on clustering-based band selection, and proposes a new framework to solve the above dilemma, claiming the following contributions: 1) An optimal clustering framework (OCF), which can obtain the optimal clustering result for a particular form of objective function under a reasonable constraint. 2) A rank on clusters strategy (RCS), which provides an effective criterion to select bands on existing clustering structure. 3) An automatic method to determine the number of the required bands, which can better evaluate the distinctive information produced by certain number of bands. In experiments, the proposed algorithm is compared to some state-of-the-art competitors. According to the experimental results, the proposed algorithm is robust and significantly outperform the other methods on various data sets

An Efficient Algorithm for Clustering of Large-Scale Mass Spectrometry Data

High-throughput spectrometers are capable of producing data sets containing thousands of spectra for a single biological sample. These data sets contain a substantial amount of redundancy from peptides that may get selected multiple times in a LC-MS/MS experiment. In this paper, we present an efficient algorithm, CAMS (Clustering Algorithm for Mass Spectra) for clustering mass spectrometry data which increases both the sensitivity and confidence of spectral assignment. CAMS utilizes a novel metric, called F-set, that allows accurate identification of the spectra that are similar. A graph theoretic framework is defined that allows the use of F-set metric efficiently for accurate cluster identifications. The accuracy of the algorithm is tested on real HCD and CID data sets with varying amounts of peptides. Our experiments show that the proposed algorithm is able to cluster spectra with very high accuracy in a reasonable amount of time for large spectral data sets. Thus, the algorithm is able to decrease the computational time by compressing the data sets while increasing the throughput of the data by interpreting low S/N spectra.Comment: 4 pages, 4 figures, Bioinformatics and Biomedicine (BIBM), 2012 IEEE International Conference o

DRSP : Dimension Reduction For Similarity Matching And Pruning Of Time Series Data Streams

Similarity matching and join of time series data streams has gained a lot of relevance in today's world that has large streaming data. This process finds wide scale application in the areas of location tracking, sensor networks, object positioning and monitoring to name a few. However, as the size of the data stream increases, the cost involved to retain all the data in order to aid the process of similarity matching also increases. We develop a novel framework to addresses the following objectives. Firstly, Dimension reduction is performed in the preprocessing stage, where large stream data is segmented and reduced into a compact representation such that it retains all the crucial information by a technique called Multi-level Segment Means (MSM). This reduces the space complexity associated with the storage of large time-series data streams. Secondly, it incorporates effective Similarity Matching technique to analyze if the new data objects are symmetric to the existing data stream. And finally, the Pruning Technique that filters out the pseudo data object pairs and join only the relevant pairs. The computational cost for MSM is O(l*ni) and the cost for pruning is O(DRF*wsize*d), where DRF is the Dimension Reduction Factor. We have performed exhaustive experimental trials to show that the proposed framework is both efficient and competent in comparison with earlier works.Comment: 20 pages,8 figures, 6 Table