Muhamad Taufik Abdullah

Region-based facial expression recognition in still images.

In Facial Expression Recognition Systems (FERS), only particular regions of the face are utilized for discrimination. The areas of the eyes, eyebrows, nose, and mouth are the most important features in any FERS. Applying facial features descriptors such as the local binary pattern (LBP) on such areas results in an effective and efficient FERS. In this paper, we propose an automatic facial expression recognition system. Unlike other systems, it detects and extracts the informative and discriminant regions of the face (i.e., eyes, nose, and mouth areas) using Haar-feature based cascade classifiers and these region-based features are stored into separate image files as a preprocessing step. Then, LBP is applied to these image files for facial texture representation and a feature-vector per subject is obtained by concatenating the resulting LBP histograms of the decomposed region-based features. The one-vs.-rest SVM, which is a popular multi-classification method, is employed with the Radial Basis Function (RBF) for facial expression classification. Experimental results show that this approach yields good performance for both frontal and near-frontal facial images in terms of accuracy and time complexity. Cohn-Kanade and JAFFE, which are benchmark facial expression datasets, are used to evaluate this approach.

Query translation using concepts similarity based on Quran ontology for cross-language information retrieval.

In Cross-Language Information Retrieval (CLIR) process, the translation effects have a direct impact o n the accuracy of follow-up retrieval results. In diction ary-based approach, we are dealing with the words t hat have more than one meaning which can decrease the retrieval performance if the query translation retur n an incorrect translations. These issues need to be ove rcome using efficient technique. In this study we p roposed a Cross-Language Information Retrieval (CLIR) method based on domain ontology using Quran concepts for disambiguating translation of the query and to improve the dictionary-based query translation. For experimentation, we use Quran ontology written in E nglish and Malay languages as a bilingual parallelcorpora and Quran concepts as a resource for cross- language query translation along with dictionary-ba sed translation. For evaluation, we measure the perform ance of three IR systems. IR 1 is natural language query IR, IR 2 is natural language query CLIR based on dictionary (as a Baseline) and IR 3 is the retrieval of this research proposed method using Mean Average Precision (MAP) and average precision at 11 points of recall. The experimental result shows that our prop osed method brings significant improvement in retri eval accuracy for English document collections, but defi cient for Malay document collections. The proposed CLIR method can obtain query expansion effect and improve retrieval performance in certain language.

Application of latent semantic indexing on Malay-English cross language information retrieval

This paper concerns an application of latent semantic indexing on Malay and English cross language information retrieval system. The retrieval effectiveness was tested on the actual Quranic collection using latent semantic indexing model. The results show that average precision on cross language is higher than monolingual retrieval.

Query translation architecture for Malay-English Cross-Language information retrieval system

This paper discusses research on query translation events in Malay-English Cross-Language Information Retrieval (CLIR) system. We assume that by improving query translation accuracy, we can improve the information retrieval performance. The dictionary-based CLIR system facing three main problems: translation ambiguity; compound and phrase handling and proper names translation. The use of natural language processing (NLP) techniques, such as stemming, Part-of-Speech (POS) tagging is useful in query translation process. Hence, n-gram matching technique has successfully applied to information retrieval (IR) system for phrases and proper names translation. The proposed query translation architecture consist of stemming, Part-of-Speech (POS) tagging and n-gram matching techniques is useful in CLIR system as well as search engine application.

A review on the cross-lingual information retrieval

Information retrieval involves finding some required information in a collection of information or in database. The collection not necessarily be in one language only as information does not limited to language. The simplest way to search for the information is to look at every item in the collection and when the need to translate the languages being used arises, this is where the techniques and methods that were being developed for the cross-lingual retrieval system will take place. This article reviews some recent researches focusing on topics in cross-lingual information retrieval and their role in current research directions in the wide area of information retrieval.

Evaluation of Quranic text retrieval system based on manually indexed topics

This paper investigates the effectiveness of a state of the art information retrieval (IR) system in the verse retrieval problem for Quranic text. The evaluation is based on manually indexed topics of the Quran that provides both the queries and the relevance judgments. Furthermore, the system is evaluated in both Malay and English environment. The performance of the system is measured based on the MAP, the precision at 1, 5 and 10, and the MRR scores. The results of the evaluation are promising, showing the IR system has many potential for the Quranic text retrieval.

An effective fusion model for image retrieval

In the past decade, the popular Bag of Visual Words approach has been applied to many computer vision tasks, including image classification, video search, robot localization, and texture recognition. Unfortunately, most approaches use intensity features and discard color information, an important characteristic of any image that is motivated by human vision. Besides, if background colors are higher than foreground ones, Dominant Color Descriptor (DCD) retrieves images that contain similar background colors correctly. On the other hand, just color feature extraction is not sufficient for similar objects with different color descriptors (e.g. white dog vs. black dog). To solve these problems, a new Salient DCD (SDCD) color descriptor is proposed to extract foreground color and add semantic information into DCD based on the color distances and salient object extraction methods. Besides, a new fusion model is presented to fuse SDCD histogram and PHOW MSDSIFT histogram. Performance evaluation on several datasets proves that the new approach outperforms other existing, state-of-the-art methods.

English and Malay cross-lingual sentiment lexicon acquisition and analysis

Sentiment analysis finds opinions, sentiments or emotions in user-generated contents. Most efforts are focusing on the English language, for which a large amount of sources and tools for sentiment analysis are available. The objective of this paper is to introduce a cross-lingual sentiment lexicon acquisition method for the Malay and English languages and further being test on a set of news test collections. Several part of speech tags are being experimented using the Word Score Summation technique in order to classify the sentiment of the news articles. This method records up to 50% as experimental accuracy result and works better for verbs and negations in both the English and Malay news articles.

BoVW Model for Animal Recognition: An Evaluation on SIFT Feature Strategies

Nowadays classifying images into categories have taken a lot of interests in both research and practice. Content Based Image Retrieval (CBIR) was not successful in solving semantic gap problem. Therefore, Bag of Visual Words (BoVW) model was created for quantizing different visual features into words. SIFT detector is invariant and robust to translation, rotations, scaling and partially invariant to affine distortion and illumination changes. The aim of this paper is to investigate the potential usage of BoVW Word model in animal recognition. The better SIFT feature extraction method for pictures of the animal was also specified. The performance evaluation on several SIFT feature strategies validates that MSDSIFT feature extraction will get better results.

A Novel Four-Directional Thresholding Approach for Lung Computed-Tomography Images by Using Similarity-Based Segmentation Technique

In automated pulmonary nodules extraction and lung disease diagnosis by image processing techniques, image segmentation is utilized as a primary and the most essential step of lung tumour analysis. But due to extensive similarity between pulmonary vessels, bronchus and arteries in lung region and the low contrast of the Computed-Tomography (CT) image the accuracy of lung tumour diagnosis is highly dependent on the precision of segmentation. Therefore, precise lung CT image segmentation has become a challenging preprocessing task for every lung disease pathological application. In this study, a novel Four-Directional Thresholding (FDT) technique is introduce d. This propounded technique segments the pulmonary parenchyma in Computed-Tomography (CT) images using the Similarity-Based Segmentation (SBS). The proposed technique aims to augment the precision of the CT image thresholding by implementing an advanced thresholding approach from four different directions in which the determination of pixels’ value as being either on foreground or background is highly dependent on its adjacent pixel’s intensity value and the final decision is made based on all four directions’ thresholding results. In this study the importance of neighbour pixels in precision of thresholding with FDT technique is demonstrated and the effectiveness of FDT method has been evaluated on different CT images. Eventually the result of segmentation using FDT method is compared by other precursors techniques, which corroborates the high exactitude of proposed technique.