Jeremiah D. Deng

A Study on Feature Analysis for Musical Instrument Classification

In tackling data mining and pattern recognition tasks, finding a compact but effective set of features has often been found to be a crucial step in the overall problem-solving process. In this paper, we present an empirical study on feature analysis for recognition of classical instrument, using machine learning techniques to select and evaluate features extracted from a number of different feature schemes. It is revealed that there is significant redundancy between and within feature schemes commonly used in practice. Our results suggest that further feature analysis research is necessary in order to optimize feature selection and achieve better results for the instrument recognition problem.

Keypoint-Based Keyframe Selection

Keyframe selection has been crucial for effective and efficient video content analysis. While most of the existing approaches represent individual frames with global features, we, for the first time, propose a keypoint-based framework to address the keyframe selection problem so that local features can be employed in selecting keyframes. In general, the selected keyframes should both be representative of video content and containing minimum redundancy. Therefore, we introduce two criteria, coverage and redundancy, based on keypoint matching in the selection process. Comprehensive experiments demonstrate that our approach outperforms the state of the art.

A deterministic energy-efficient clustering protocol for wireless sensor networks

Wireless sensor network (WSN) technologies have been employed in recent years for monitoring purposes in various domains from engineering industry to our home environment due to their ability to intelligently monitor remote locations. In this paper, we have developed a purely deterministic model that utilizes clustering to organize the WSN. We propose a deterministic energy-efficient clustering protocol that is dynamic, distributive, self-organizing and more energy efficient than the existing protocols. It utilizes a simplified approach which minimizes computational overhead-cost to self-organize the sensor network. Our simulation result shows a better performance with respect to energy consumption, which is reflected in the network lifetime in both homogeneous and heterogeneous settings when compared with the existing protocols. It is worthy of note that our approach approximates an ideal solution for balanced energy consumption in hierarchical wireless sensor networks.

Video background modeling: recent approaches, issues and our proposed techniques

Effective and efficient background subtraction is important to a number of computer vision tasks. We introduce several new techniques to address key challenges for background modeling using a Gaussian mixture model (GMM) for moving objects detection in a video acquired by a static camera. The novel features of our proposed model are that it automatically learns dynamics of a scene and adapts its parameters accordingly, suppresses ghosts in the foreground mask using a SURF features matching algorithm, and introduces a new spatio-temporal filter to further refine the foreground detection results. Detection of abrupt illumination changes in the scene is dealt with by a model shifting-based scheme to reuse already learned models and spatio-temporal history of foreground blobs is used to detect and handle paused objects. The proposed model is rigorously tested and compared with several previous models and has shown significant performance improvements.

Enhancing Clustering in Wireless Sensor Networks with Energy Heterogeneity

While wireless sensor networks WSN are increasingly equipped to handle more complex functions, in-network processing still requires the battery-powered sensors to judiciously use their constrained energy so as to prolong the elective network life time. There are a few protocols using sensor clusters to coordinate the energy consumption in a WSN, but how to deal with energy heterogeneity remains a research question. The authors propose a modified clustering algorithm with a three-tier energy setting, where energy consumption among sensor nodes is adaptive to their energy levels. A theoretical analysis shows that the proposed modifications result in an extended network stability period. Simulation has been conducted to evaluate the new clustering algorithm against some existing algorithms under different energy heterogeneity settings, and favourable results are obtained especially when the energy levels are significantly imbalanced.

Wildlife video key-frame extraction based on novelty detection in semantic context

There is a growing evidence that visual saliency can be better modeled using top-down mechanisms that incorporate object semantics. This suggests a new direction for image and video analysis, where semantics extraction can be effectively utilized to improve video summarization, indexing and retrieval. This paper presents a framework that models semantic contexts for key-frame extraction. Semantic context of video frames is extracted and its sequential changes are monitored so that significant novelties are located using a one-class classifier. Working with wildlife video frames, the framework undergoes image segmentation, feature extraction and matching of image blocks, and then a co-occurrence matrix of semantic labels is constructed to represent the semantic context within the scene. Experiments show that our approach using high-level semantic modeling achieves better key-frame extraction as compared with its counterparts using low-level features.

A Self-adaptive CodeBook (SACB) model for real-time background subtraction

Effective and efficient background subtraction is important to a number of computer vision tasks. In this paper, we introduce a new background model that integrates several new techniques to address key challenges for background modeling for moving object detection in videos. The novel features of our proposed Self-adaptive CodeBook (SACB) background model are: a more effective color model using YCbCr color space, a statistical parameter estimation method, and a new algorithm for adding new background codewords into the permanent model and deleting noisy codewords from the models. Also, a new block-based approach is introduced to exploit the local spatial information. The proposed model is rigorously tested and has shown significant performance improvements over several previous models. This paper presents a Self-adaptive CodeBook background model for moving object segmentation in a video.Several new techniques are introduced to enhance the performance of standard CodeBook model.The proposed model gives better processing speed than the standard CodeBook model.New color model and the automatic parameter estimation mechanism help to achieve better accuracy than the standard CodeBook model.The proposed model gives a real-time performance and a good balance between segmentation accuracy and processing efficiency.

An Application-Specific Forecasting Algorithm for Extending WSN Lifetime

Data reduction strategy is one of the schemes employed to extend network lifetime. In this paper we present an implementation of a light-weight forecasting algorithm for sensed data which saves packet transmission in the network. The proposed Naive algorithm achieves high energy savings with a limited computational overhead on a node. Simulation results from realistic Building monitoring application of WSN are compared with well-known prediction algorithms such as ARIMA, LMS and WMA models. We implemented a real-world deployment using 32bit mote-class device. Overall, up to 96% transmission reduction is achieved using our Naive method, while still able to maintain a considerable level of accuracy at 0.5 °C error bound and it is comparable in performance to the more complex models such as ARIMA, LMS and WMA.

Segment-Based Predominant Learning Swarm Optimizer for Large-Scale Optimization

Large-scale optimization has become a significant yet challenging area in evolutionary computation. To solve this problem, this paper proposes a novel segment-based predominant learning swarm optimizer (SPLSO) swarm optimizer through letting several predominant particles guide the learning of a particle. First, a segment-based learning strategy is proposed to randomly divide the whole dimensions into segments. During update, variables in different segments are evolved by learning from different exemplars while the ones in the same segment are evolved by the same exemplar. Second, to accelerate search speed and enhance search diversity, a predominant learning strategy is also proposed, which lets several predominant particles guide the update of a particle with each predominant particle responsible for one segment of dimensions. By combining these two learning strategies together, SPLSO evolves all dimensions simultaneously and possesses competitive exploration and exploitation abilities. Extensive experiments are conducted on two large-scale benchmark function sets to investigate the influence of each algorithmic component and comparisons with several state-of-the-art meta-heuristic algorithms dealing with large-scale problems demonstrate the competitive efficiency and effectiveness of the proposed optimizer. Further the scalability of the optimizer to solve problems with dimensionality up to 2000 is also verified.

Novelty detection in wildlife scenes through semantic context modelling

Novelty detection is an important functionality that has found many applications in information retrieval and processing. In this paper we propose a novel framework that deals with novelty detection in multiple-scene image sets. Working with wildlife image data, the framework starts with image segmentation, followed by feature extraction and classification of the image blocks extracted from image segments. The labelled image blocks are then scanned through to generate a co-occurrence matrix of object labels, representing the semantic context within the scene. The semantic co-occurrence matrices then undergo binarization and principal component analysis for dimension reduction, forming the basis for constructing one-class models on scene categories. An algorithm for outliers detection that employs multiple one-class models is proposed. An advantage of our approach is that it can be used for novelty detection and scene classification at the same time. Our experiments show that the proposed approach algorithm gives favourable performance for the task of detecting novel wildlife scenes, and binarization of the semantic co-occurrence matrices helps increase the robustness to variations of scene statistics.