LIBPMK: A Pyramid Match Toolkit

LIBPMK is a C++ implementation of Grauman and Darrell's pyramid match algorithm. This toolkit provides a flexible framework with which developers can quickly match sets of image features and run experiments. LIBPMK provides functionality for $k$-means and hierarchical clustering, dealing with data sets too large to fit in memory, building multi-resolution histograms, quickly performing pyramid matches, and training and testing support vector machines (SVMs). This report provides a tutorial on how to use the LIBPMK code, and gives the specifications of the LIBPMK API

Fault diagnosis of rolling bearing with incomplete labels using weakly labeled support vector machine

The fault diagnosis of rolling bearing has attracted increasing attention in recent years on account of the significant impact on the functionality and efficiency of complex primary system. In consideration of the bearing samples with incomplete labels, this paper investigates the possibilities of a novel fault diagnosis method using the experience of image cognition theory in dealing with the fault state classification of rolling bearings, aiming to realize fault classification that only utilizes a small amount of labeled bearing data. In this paper empirical mode decomposition (EMD) is firstly applied to the original signal, where the basic time domain features are extracted from the first three intrinsic mode functions (IMFs), and are set as the inputs of the following classifier for final training and testing. Weakly labeled support vector machine (WELLSVM), which seems more efficient than inductive support vector machines especially in the case of very small training sets and large test sets, is then established via a novel label generation strategy in the method of semi-supervised learning. Validation data are collected to facilitate the comparison and evaluation of the fault diagnosis results, of which the labeled data proportion is diverse from each other. The results indicates the effectiveness of the proposed method for bearing fault diagnosis with weakly labeled data

Separability versus Prototypicality in Handwritten Word Retrieval

User appreciation of a word-image retrieval system is based on the quality ofa hit list for a query. Using support vector machines for ranking in largescale, handwritten document collections, we observed that many hit listssuffered from bad instances in the top ranks. An analysis of this problemrevealed that two functions needed to be optimised concerning bothseparability and prototypicality. By ranking images in two stages, the numberof distracting images is reduced, making the method very convenient formassive scale, continuously trainable retrieval engines. Instead of cumbersomeSVM training, we present a nearest-centroid method and show that precisionimprovements of up to 35 percentage points can be achieved, yielding up to100% precision in data sets with a large amount of instances, whilemaintaining high recall performances.<br/

Effects of Training Set Size on Supervised Machine-Learning Land-Cover Classification of Large-Area High-Resolution Remotely Sensed Data

The size of the training data set is a major determinant of classification accuracy. Neverthe- less, the collection of a large training data set for supervised classifiers can be a challenge, especially for studies covering a large area, which may be typical of many real-world applied projects. This work investigates how variations in training set size, ranging from a large sample size (n = 10,000) to a very small sample size (n = 40), affect the performance of six supervised machine-learning algo- rithms applied to classify large-area high-spatial-resolution (HR) (1–5 m) remotely sensed data within the context of a geographic object-based image analysis (GEOBIA) approach. GEOBIA, in which adjacent similar pixels are grouped into image-objects that form the unit of the classification, offers the potential benefit of allowing multiple additional variables, such as measures of object geometry and texture, thus increasing the dimensionality of the classification input data. The six supervised machine-learning algorithms are support vector machines (SVM), random forests (RF), k-nearest neighbors (k-NN), single-layer perceptron neural networks (NEU), learning vector quantization (LVQ), and gradient-boosted trees (GBM). RF, the algorithm with the highest overall accuracy, was notable for its negligible decrease in overall accuracy, 1.0%, when training sample size decreased from 10,000 to 315 samples. GBM provided similar overall accuracy to RF; however, the algorithm was very expensive in terms of training time and computational resources, especially with large training sets. In contrast to RF and GBM, NEU, and SVM were particularly sensitive to decreasing sample size, with NEU classifications generally producing overall accuracies that were on average slightly higher than SVM classifications for larger sample sizes, but lower than SVM for the smallest sample sizes. NEU however required a longer processing time. The k-NN classifier saw less of a drop in overall accuracy than NEU and SVM as training set size decreased; however, the overall accuracies of k-NN were typically less than RF, NEU, and SVM classifiers. LVQ generally had the lowest overall accuracy of all six methods, but was relatively insensitive to sample size, down to the smallest sample sizes. Overall, due to its relatively high accuracy with small training sample sets, and minimal variations in overall accuracy between very large and small sample sets, as well as relatively short processing time, RF was a good classifier for large-area land-cover classifications of HR remotely sensed data, especially when training data are scarce. However, as performance of different supervised classifiers varies in response to training set size, investigating multiple classification algorithms is recommended to achieve optimal accuracy for a project

Image Segmentation and 3-D Reconstruction of Coronary Artery

Segmentation of arterial wall boundaries from biomedical images is an important issue for many applications such as in the study of plaque characteristics, to extract mechanical properties of the arterial wall, its 3-D construction, and the measurements such as wall radius, lumen radius and lumen size. So here we present a solution to segmentation of images of coronary arteries and 3D construction. The structure introduced here is a set of connected vertices. An initial contour model which is defined here is allowed to deform according to an energy minimizing term with minimum number of iterations. The energy associated with the contours are depends on curvature of contour, and image features. Using training data in a properly built shape space, we are able to classify media and adventitia walls approximately using a large set of data sets using Support Vector Machines. The 3D construction of artery using point matching of successive frames is also explained here with less complexity. The tests of the presented algorithm on a large set of data depicts the effectiveness of this approach