Syed Abdullah Fadzli

Semantic approach to image retrieval using statistical models based on a lexical ontology

The increasing amount of digital images available on the Internet has made searching, browsing, and organizing such resources a major challenge. This paper proposes a semantic approach to text-based image retrieval of manually annotated digital images. The approach uses statistical models based on Semantic DNA (SDNA) extracted from the structure of a lexical ontology called OntoRo. The approach involves three main techniques: (a) SDNA extraction, (b) word sense disambiguation using statistical models based on the extracted SDNA, and (c) applying semantic similarity measures using SDNA. The experiments performed show that the proposed approach retrieves images based on their conceptual meaning rather than the use of specific keywords in their annotations.

Robotic Indoor Path Planning Using Dijkstra's Algorithm with Multi-Layer Dictionaries

Dijkstras algorithm is a classic algorithm for finding the shortest path between two points due to its optimisation capability. The adjacency matrix is the naive storage structure of the algorithm. This storage structure has limited the use of the algorithm as it expands large storage space. A multi-layer dictionary is proposed in this work to enhance the storage structure. Previously, the algorithm was used to optimise single parameter (such as distance, time and fuel) for movement between two places. The path computed using the classic Dijkstras algorithm is the shortest; however, it may not be the most feasible. The algorithm needs to optimise other factors such as energy consumption and the degree of turns a robot need to take. Junction degree of difficulty function is introduced to further improve the output. It is defined as the degree of difficulty of the path between two points in an open environment. The experimental result shows that the proposed path planning method produced the most optimal path between two points when applied to a map of any indoor terrain.

Ontology-based indexing of annotated images using semantic DNA and vector space model

The study presented in this paper focuses on the preprocessing stage of image retrieval by proposing an ontology-based indexing approach which captures the meaning of image annotations by extracting the semantic importance of the words in them. The indexing algorithm is based on the classic vector-space model that is adapted by employing index weighting and a word sense disambiguation. It uses sets of Semantic DNA, extracted from a lexical ontology, to represent the images in a vector space. As discussed in the paper, the use of Semantic DNA in text-based image retrieval aims to overcome some of the major drawbacks of well known traditional approaches such as ‘bags of words’ and term frequency-(TF) based indexing. The proposed approach is evaluated by comparing the indexing achieved using the proposed semantic algorithm with results obtained using a traditional TF-based indexing in vector space model (VSM) with singular value decomposition (SVD) technique. The experimental results show that the proposed ontology-based approach generates a better-quality index which captures the conceptual meaning of the image annotations.

A systematic survey of martial art using motion capture technologies: the importance of extrinsic feedback

The number of research papers on Motion Capture technologies published in conferences and journals has been rapidly increasing due to the emerging of new technologies, software and hardware which create new challenges and opportunities for Martial Arts research. Current trend of the Martial Arts using Motion Capture technologies (MAMoCap) researches consists of phases of MoCap-Processing and Post-MoCap-Processing; contexts of algorithms, performance and system development; and feedbacks of intrinsic and extrinsic. The purpose of this paper is to study and explore the potential future trend of research and publications pertaining to MAMoCap researches. A systematic survey of research publications was conducted through the topic of Martial Art (MA) and Motion Capture (MoCap) in order to retrieve the scientific articles published in FOUR (4) established publishers including SPRINGERLINK, SCIENCEDIRECT, IEEE and ACM. Search refinements were done by the inclusions criteria of document types of academic journals and conference proceedings; and by the exceptions criteria of letters, editorials and book reviews. The findings show that only 27% of the publications have been selected while other 73% have been classified as irrelevant contents due to none significance and relevance to the MAMoCap researches. Analysis on the research phases, contexts and feedbacks has been conducted and discussed in detailed for pertaining knowledge gaps and future research agenda. Based on the preliminary study, a framework of EFs-Based Automated Evaluation System for the martial arts should be proposed.