7400363
Empirical Studies on Multi-label Classification
In classic pattern recognition problems, classes are mutually exclusive by definition. However, in many applications, it is quite natural that some instances belong to multiple classes at the same time. In other words, these applications are multi-labeled, classes are overlapped by definition and each instance may be associated to multiple classes. In this paper, we present a comparative study on various multi-label approaches using both gene and scene data sets. We expect our research efforts provide useful insights on the relationships among various classifiers as well as various evaluation measures and shed lights on future research. Although there is no clear winner across various performance measures, SVM binary and multi-label ADTree perform better than the others on most counts. We then propose a meta-learning approach by combining SVM binary and ADTree. Our experiments demonstrate that the combined method can take the advantages of the single approaches
8864863
Integrating Features from Different Sources for Music Information Retrieval
Efficient and intelligent music information retrieval is a very important topic of the 21st century. With the ultimate goal of building personal music information retrieval systems, this paper studies the problem of identifying "similar" artists using both lyrics and acoustic data. In this paper, we present a clustering algorithm that integrates features from both sources to perform bimodal learning. The algorithm is tested on a data set consisting of 570 songs from 53 albums of 41 artists using artist similarity provided by All Music Guide. Experimental results show that the accuracy of artist similarity classifiers can be significantly improved and that artist similarity can be efficiently identified.
37613840
Improving medical/biological data classification performance by wavelet preprocessing
Many real-world datasets contain noise which could degrade the performances of learning algorithms. Motivated from the success of wavelet denoising techniques in image data, we explore a general solution to alleviate the effect of noisy data by wavelet preprocessing for medical/biological data classification. Our experiments are divided into two categories: one is of different classification algorithms on a specific database, and the other is of a specific classification algorithm (decision tree) on different databases. The experiment results show that the wavelet denoising of noisy data is able to improve the accuracies of those classification methods, if the localities of the attributes are strong enough.
9813108
CoFD : An Algorithm for Non-distance Based Clustering in High Dimensional Spaces
The clustering problem, which aims at identifying the distribution of patterns and intrinsic correlations in large data sets by partitioning the data points into similarity clusters, has been widely studied. Traditional clustering algorithms use distance functions to measure similarity and are not suitable for high dimensional spaces. In this paper, we propose CoFD algorithm, which is a non-distance based clustering algorithm for high dimensional spaces. Based on the maximum likelihood principle, CoFD is to optimize parameters to maximize the likelihood between data points and the model generated by the parameters. Experimental results on both synthetic data sets and a real data set show the efficiency and effectiveness of CoFD.
1527832
Document clustering with cluster refinement and model selection capabilities
In this paper, we propose a document clustering method that strives to achieve: (1) a high accuracy of document clustering, and (2) the capability of estimating the number of clusters in the document corpus (i.e. the model selection capability). To accurately cluster the given document corpus, we employ a richer feature set to represent each document, and use the Gaussian Mixture Model (GMM) together with the Expectation-Maximization (EM) algorithm to conduct an initial document clustering. From this initial result, we identify a set of discriminative featuresfor each cluster, and refine the initially obtained document clusters by voting on the cluster label of each document using this discriminative feature set. This self-refinement process of discriminative feature identification and cluster label voting is iteratively applied until the convergence of document clusters. On the other hand, the model selection capability is achieved by introducing randomness in the cluster initialization stage, and then discovering a value C for the number of clusters N by which running the document clustering process for a fixed number of times yields sufficiently similar results. Performance evaluations exhibit clear superiority of the proposed method with its improved document clustering and model selection accuracies. The evaluations also demonstrate how each feature as well as the cluster refinement process contribute to the document clustering accuracy.
15097946
Algorithms for clustering high dimensional and distributed data
Clustering is the problem of identifying the distribution of patterns and intrinsic correlations in large data sets by partitioning the data points into similarity classes. The clustering problem has been widely studied in machine learning, databases, and statistics. This paper studies the problem of clustering high dimensional data. The paper proposes an algorithm called the CoFD algorithm, which is a non-distance based clustering algorithm for high dimensional spaces. Based on the Maximum Likelihood Principle, CoFD attempts to optimize its parameter settings to maximize the likelihood between data points and the model generated by the parameters. The distributed versions of the problem, called the D-CoFD algorithms, are also proposed. Experimental results on both synthetic and real data sets show the efficiency and effectiveness of CoFD and D-CoFD algorithms.
11176389
Incorporating visual knowledge representation in stereo reconstruction
In this paper, we present a two-layer generative model that incorporates generic middle-level visual knowledge for dense stereo reconstruction. The visual knowledge is represented by a dictionary of surface primitives including various categories of boundary discontinuities and junctions in parametric form. Given a stereo pair, we first compute a primal sketch representation which decomposes the image into a structural part for object boundaries and high intensity contrast represented by a 2D sketch graph, and a structure less part represented by Markov random field on pixels. Then we label the sketch graph and compute the 3D sketch (like a wire-frame) by fitting the primitive dictionary to the sketch graph. The surfaces between the 3D sketches are filled in by computing the depth of the MRP model on the structureless part. These two levels interact closely since the MRF is used to propagate information between the primitives, and at the same time, the primitives act as boundary conditions for the MRF. The two processes maximize a Bayesian posterior probability jointly. We propose an MCMC algorithm that simultaneously infers the 3D primitive types and parameters and estimates the depth of the scene. Our experiments show that this representation can infer the depth map with sharp boundaries and junctions for textureless images, curve objects and free-form shapes.
206590834
Composite Templates for Cloth Modeling and Sketching
Cloth modeling and recognition is an important and challenging problem in both vision and graphics tasks, such as dressed human recognition and tracking, human sketch and portrait. In this paper, we present a context sensitive grammar in an And-Or graph representation which will produce a large set of composite graphical templates to account for the wide variabilities of cloth configurations, such as T-shirts, jackets, etc. In a supervised learning phase, we ask an artist to draw sketches on a set of dressed people, and we decompose the sketches into categories of cloth and body components: collars, shoulders, cuff, hands, pants, shoes etc. Each component has a number of distinct subtemplates (sub-graphs). These sub-templates serve as leafnodes in a big And-Or graph where an And-node represents a decomposition of the graph into sub-configurations with Markov relations for context and constraints (soft or hard), and an Or-node is a switch for choosing one out of a set of alternative And-nodes (sub-configurations) - similar to a node in stochastic context free grammar (SCFG). This representation integrates the SCFG for structural variability and the Markov (graphical) model for context. An algorithm which integrates the bottom-up proposals and the topdown information is proposed to infer the composite cloth template from the image.
41478916
New Uniform Colour Spaces
2266681
Thermal Medical Image Retrieval by Moment Invariants
Thermal medical imaging provides a valuable method for detecting various diseases such as breast cancer or Raynaud’s syndrome. While previous efforts on the automated processing on thermal infrared images were designed for and hence constrained to a certain type of disease we apply the concept of content-based image retrieval (CBIR) as a more generic approach to the problem. CBIR allows the retrieval of similar images based on features extracted directly from image data. Image retrieval for a thermal image that shows symptoms of a certain disease will provide visually similar cases which usually also represent similarities in medical terms. The image features we investigate in this study are a set of combinations of geometric image moments which are invariant to translation, scale, rotation and contrast.
17718282
A Stochastic Algorithm for 3D Scene Segmentation and Reconstruction
In this paper, we present a stochastic algorithm by effective Markov chain Monte Carlo (MCMC) for segmenting and reconstructing 3D scenes. The objective is to segment a range image and its associated reflectance map into a number of surfaces which fit to various 3D surface models and have homogeneous reflectance (material) properties. In comparison to previous work on range image segmentation, the paper makes the following contributions. Firstly, it is aimed at generic natural scenes, indoor and outdoor, which are often much complexer than most of the existing experiments in the "polyhedra world". Natural scenes require the algorithm to automatically deal with multiple types (families) of surface models which compete to explain the data. Secondly, it integrates the range image with the reflectance map. The latter provides material properties and is especially useful for surface of high specularity, such as glass, metal, ceramics. Thirdly, the algorithm is designed by reversible jump and diffusion Markov chain dynamics and thus achieves globally optimal solutions under the Bayesian statistical framework. Thus it realizes the cue integration and multiple model switching. Fourthly, it adopts two techniques to improve the speed of the Markov chain search: One is a coarse-to-fine strategy and the other are data driven techniques such as edge detection and clustering. The data driven methods provide important information for narrowing the search spaces in a probabilistic fashion. We apply the algorithm to two data sets and the experiments demonstrate robust and satisfactory results on both. Based on the segmentation results, we extend the reconstruction of surfaces behind occlusions to fill in the occluded parts.