M. K. M. Rahman

Multilayer SOM With Tree-Structured Data for Efficient Document Retrieval and Plagiarism Detection

This paper proposes a new document retrieval (DR) and plagiarism detection (PD) system using multilayer self-organizing map (MLSOM). A document is modeled by a rich tree-structured representation, and a SOM-based system is used as a computationally effective solution. Instead of relying on keywords/lines, the proposed scheme compares a full document as a query for performing retrieval and PD. The tree-structured representation hierarchically includes document features as document, pages, and paragraphs. Thus, it can reflect underlying context that is difficult to acquire from the currently used word-frequency information. We show that the tree-structured data is effective for DR and PD. To handle tree-structured representation in an efficient way, we use an MLSOM algorithm, which was previously developed by the authors for the application of image retrieval. In this study, it serves as an effective clustering algorithm. Using the MLSOM, local matching techniques are developed for comparing text documents. Two novel MLSOM-based PD methods are proposed. Detailed simulations are conducted and the experimental results corroborate that the proposed approach is computationally efficient and accurate for DR and PD.

A new document representation using term frequency and vectorized graph connectionists with application to document retrieval

This paper presents a new document representation with vectorized multiple features including term frequency and term-connection-frequency. A document is represented by undirected and directed graph, respectively. Then terms and vectorized graph connectionists are extracted from the graphs by employing several feature extraction methods. This hybrid document feature representation more accurately reflects the underlying semantics that are difficult to achieve from the currently used term histograms, and it facilitates the matching of complex graph. In application level, we develop a document retrieval system based on self-organizing map (SOM) to speed up the retrieval process. We perform extensive experimental verification, and the results suggest that the proposed method is computationally efficient and accurate for document retrieval.

A new image classification technique using tree-structured regional features

Image classification is a challenging problem of computer vision. Conventional image classification methods use flat image features with fixed dimensions, which are extracted from a whole image. Such features are computationally effective but are crude representation of the image content. This paper proposes a new image classification approach through a tree-structured feature set. In this approach, the image content is organized in a two-level tree, where the root node at the top level represents the whole image and the child nodes at the bottom level represent the homogeneous regions of the image. The tree-structured representation combines both the global and the local features through the root and the child nodes. The tree-structured feature data are then processed by a two-level self-organizing map (SOM), which consists of an unsupervised SOM for processing image regions and a supervising concurrent SOM (CSOM) classifier for the overall classification of images. The proposed method incorporates both global image features and local region-based features to improve the performance of image classification. Experimental results show that this approach performs better than conventional approaches.

Content-based image retrieval using growing hierarchical self-organizing quadtree map

In this paper, a growing hierarchical self-organizing quadtree map (GHSOQM) is proposed and used for a content-based image retrieval (CBIR) system. The incorporation of GHSOQM in a CBIR system organizes images in a hierarchical structure. The retrieval time by GHSOQM is less than that by using direct image comparison using a flat structure. Furthermore, the ability of incremental learning enables GHSOQM to be a prospective neural-network-based approach for CBIR systems. We also propose feature matrices, image distance and relevance feedback for region-based images in the GHSOQM-based CBIR system. Experimental results strongly demonstrate the effectiveness of the proposed system.

A flexible multi-layer self-organizing map for generic processing of tree-structured data

A new multi-layer self-organizing map (MLSOM) is proposed for unsupervised processing tree-structured data. The MLSOM is an improved self-organizing map for handling structured data. By introducing multiple SOM layers, the MLSOM can overcome the computational speed and visualization problems of SOM for structured data (SOM-SD). Node data in different levels of a tree are processed in different layers of the MLSOM. Root nodes are dedicatedly processed on the top SOM layer enabling the MLSOM a better utilization of SOM map compared with the SOM-SD. Thus, the MLSOM exhibits better data organization, clustering, visualization, and classification results of tree-structured data. Experimental results on three different data sets demonstrate that the proposed MLSOM approach can be more efficient and effective than the SOM-SD.

A new dual wing harmonium model for document retrieval

A new dual wing harmonium model that integrates term frequency features and term connection features into a low dimensional semantic space without increase of computation load is proposed for the application of document retrieval. Terms and vectorized graph connectionists are extracted from the graph representation of document by employing weighted feature extraction method. We then develop a new dual wing harmonium model projecting these multiple features into low dimensional latent topics with different probability distributions assumption. Contrastive divergence algorithm is used for efficient learning and inference. We perform extensive experimental verification, and the comparative results suggest that the proposed method is accurate and computationally efficient for document retrieval.

Motor fault detection using vibration patterns

This work presents a novel method for fault detection of electrical motors using vibration signal. Most of the motor faults generate specific patterns in the motor vibration that can be captured and analyzed for diagnosis. Early detection of motor faults can save the motor from subsequent deteriorations into more severe conditions, and thus can save lot of maintenance costs. In our work, an optical mouse was used to capture decently accurate information of the motor-vibration. Features are extracted in time and frequency domain using which an Artificial Neural Network (ANN) called Multi-Layer Perceptron (MLP) was trained to learn different motor conditions such as healthy and faulty. A MATLAB-based user interface was developed to record, monitor, analyze and classify the motor vibration data. This study shows that using simple features and ANN structure can effectively and efficiently classify different types of motor faults. The use of low-cost mouse sensor has made this method very attractive to wide range of applications where a cost-effective solution is desired.

Content-based hierarchical document organization using multi-layer hybrid network and tree-structured features

Automatic organizing documents through a hierarchical tree is demanding in many real applications. In this work, we focus on the problem of content-based document organization through a hierarchical tree which can be viewed as a classification problem. We proposed a new document representation to enhance the classification accuracy. We developed a new hybrid neural network model to handle the new document representation. In our document representation, a document is represented by a tree-structure that has a superior capability of encoding document characteristics. Compared to traditional feature representation that encodes only global characteristics of a document, the proposed approach can encode both global and local characteristics of a document through a hierarchical tree. Unlike traditional representation, the tree representation reflects the spatial organizations of words through pages and paragraphs of a document that help to encode better semantics of a document. Processing hierarchical tree is another challenging task in terms of computational complexity. We developed a hybrid neural network model, composed of SOM and MLP, for this task. Experimental results corroborate that our approach is efficient and effective in registering documents into organized tree compared with other approach.

Face Matching in Large Database by Self-Organizing Maps

A novel self-organizing map (SOM) based retrieval system is proposed for performing face matching in large database. The proposed system provides a small subset of faces that are most similar to a given query face, from which user can easily verify the matched images. The architecture of the proposed system consists of two major parts. First, the system provides a generalized integration of multiple feature-sets using multiple self-organizing maps. Multiple feature-sets are obtained from different feature extraction methods like Gabor filter, Local Autocorrelation Coefficients, etc. In this platform, multiple facial features are integrated to form a compressed feature vector without concerning scaling and length of individual feature set. Second, an SOM is trained to organize all the face images in a database through using the compressed feature vector. Using the organized map, similar faces to a query can be efficiently identified. Furthermore, the system includes a relevance feedback to enhance the face retrieval performance. The proposed method is computationally efficient. Comparative results show that the proposed approach is promising for identifying face in a given large image database.

A segmentation based approach for shape recovery from multi-color images

Conventional shape from shading (SFS) algorithms are unable to deal with multi-color image satisfactory. This is because the assumption of constant surface albedo in the algorithms is not applicable to multi-color images. This paper proposes a new SFS approach for multi-color images through a segmentation-based shading recovery technique. With this technique a gray image is firstly extracted from the multi-color image containing better shading information compared with other color-to-gray conversion methods. The shading is recovered in the gray image as if the objects were made of single color. Shape of the multi-color object can then be recovered by classical gray-scaled SFS methods. Experimental results with synthetic and real multi-color images are presented. The obtained results corroborate that the proposed scheme is able to deliver better performance compared with other color SFS methods.