Mohd Norhisham Razali

A Visualization Approach to Automatic Text Documents Categorization Based on HAC

The ability to visualize documents into clusters is very essential. The best data summarization technique could be used to summarize data but a poor representation or visualization of it will be totally misleading. As proposed in many researches, clustering techniques are applied and the results are produced when documents are grouped in clusters. However, in some cases, user may want to know the relationship that exists between clusters. In order to illustrate relationships that exist between clusters, a hierarchical agglomerative clustering (HAC) technique can be applied to build the dendrogram. The dendrogram produced display the relationship between a cluster and its sub-clusters. For this reason, user will be able to view the relationship that exists between clusters. In addition to that, the terms or features that characterize each cluster can also be displayed to assist user in understanding the contents of whole text documents that stored in the database. In this paper, a Text Analyzer (VisualText) that automates the categorization of text documents based on a visualization approach using the Hierarchical Agglomerative Clustering technique is proposed. This paper also studies the effect of using different inter-cluster proximities on the quality of clusters produced. Cophenetic Correlation Coefficient is measured in order to evaluate the quality of clusters produced using these three different inter-cluster distance measurements.

A Rule-Based Named-Entity Recognition for Malay Articles

A Named-Entity Recognition (NER) is part of the process in Text Mining used for information extraction. This NER tool can be used to assist user in identifying and detecting entities such as person, location or organization. Different languages may have different morphologies and thus require different NER processes. For instance, an English NER process cannot be applied in processing Malay articles due to the different morphology used in different languages. This paper proposes a Rule-Based Named-Entity Recognition algorithm for Malay articles. The proposed Malay NER is designed based on a Malay part-of-speech (POS) tagging features and contextual features that had been implemented to handle Malay articles. Based on the POS results, proper names will be identified or detected as the possible candidates for annotation. Besides that, there are some symbols and conjunctions that will also be considered in the process of identifying named-entity for Malay articles. Several manually constructed dictionaries will be used to handle three named-entities; Person, Location and Organizations. The experimental results show a reasonable output of 89.47% for the F-Measure value. The proposed Malay NER algorithm can be further improved by having more complete dictionaries and refined rules to be used in order to identify the correct Malay entities system.

Food Category Recognition Using SURF and MSER Local Feature Representation

Food object recognition has gained popularity in recent years. This can perhaps be attributed to its potential applications in fields such as nutrition and fitness. Recognizing food images however is a challenging task since various foods come in many shapes and sizes. Besides having unexpected deformities and texture, food images are also captured in differing lighting conditions and camera viewpoints. From a computer vision perspective, using global image features to train a supervised classifier might be unsuitable due to the complex nature of the food images. Local features on the other hand seem the better alternative since they are able to capture minute intricacies such as interest points and other intricate information. In this paper, two local features namely SURF (Speeded- Up Robust Feature) and MSER (Maximally Stable Extremal Regions) are investigated for food object recognition. Both features are computationally inexpensive and have shown to be effective local descriptors for complex images. Specifically, each feature is firstly evaluated separately. This is followed by feature fusion to observe whether a combined representation could better represent food images. Experimental evaluations using a Support Vector Machine classifier shows that feature fusion generates better recognition accuracy at 86.6%.

An integration of minimum local feature representation methods to recognize large variation of foods

Local invariant features have shown to be successful in describing object appearances for image classification tasks. Such features are robust towards occlusion and clutter and are also invariant against scale and orientation changes. This makes them suitable for classification tasks with little inter-class similarity and large intra-class difference. In this paper, we propose an integrated representation of the Speeded-Up Robust Feature (SURF) and Scale Invariant Feature Transform (SIFT) descriptors, using late fusion strategy. The proposed representation is used for food recognition from a dataset of food images with complex appearance variations. The Bag of Features (BOF) approach is employed to enhance the discriminative ability of the local features. Firstly, the individual local features are extracted to construct two kinds of visual vocabularies, representing SURF and SIFT. The visual vocabularies are then concatenated and fed into a Linear Support Vector Machine (SVM) to classify the respective fo...

An Automatic Spell Checker Framework for Malay Language Blogs

A Spell checker is a system that is used to detect and correct misspelled word. Misspelled word is a word that exists in the existing lexicon that is not correctly spelled or in shortened form. These misspelled words often result in ineffective results of the Information Retrieval (IR) application such as document retrieval. This is because IR application should be able to recognize all words in a particular language in order to be more robust. The current spell checker for the Malay language uses a dictionary that contains pair of commonly misspelled word and its correctly spelled word in detecting and correcting misspelled word. However, this type of spell checker can only correct misspelled words that exist in the existing dictionary; otherwise it requires user interaction to correct it manually. This approach works well if the spell checker is a standalone system but it is not really an effective system when the spell checker is part of another IR application such as document retrieval for weblog. This is because there will be always new misspelled words created along with the increasing number of weblog pages. Thus, the number of misspelled words will also grow extremely. In this paper, we propose a new spell checker that detects and automatically corrects misspelled words in Malay without any interaction from the user. The proposed approach is evaluated by using texts that are selected randomly from the popular Malay blog. Based on the experimental results obtained, the proposed approach is found to be effective in detecting and correcting the Malay misspelled word automatically.