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Papers by Abass Olaode

2021 10th International Conference on Software and Computer Applications, 2021

A smart wheelchair requires a security system for its users to feel safe and comfortable. The pro... more A smart wheelchair requires a security system for its users to feel safe and comfortable. The process of observing road conditions is one of the solutions to maintaining user safety, which one of these hurdles can be a sudden transition of the situation in surface road height level for example, such as a descending staircase. Integration system for safety in smart wheelchairs consists of three main parts, namely input (Camera), output (Driver motor Left and Right), and main processing (Mini PC). The proposed research will be carried out stair decent detection using a Gray Level Co-occurrence matrix (GLCM) algorithm method as an extraction feature algorithm. The usage of GLCM methods can be applied to images that have textures. While if we look at the descent of the stairs also has a different texture when compared to the usual floor. Support Vector Machine (SVM) is used for the classification of stairs descent and floor. SVM algorithms have advantageous in it is effortless and strong consistency of implementation in classification. In this research propose combination methods between the texture features using GLCM and classification method using SVM to obtain effective detection stairs descent and floor.The proposed method by setting the GLCM parameter with a value of d = 1 and θ = 135o, and SVM classification using the Radial Basis Function Kernel (RBF Kernel) has an accuracy of 87 for detecting the stair descent with relatively fast computation time equal to 0.007 second.

IET Image Processing

The massive amount of digital content generated daily in the modern world has created the need fo... more The massive amount of digital content generated daily in the modern world has created the need for an image retrieval system built on image analysis via image processing and machine learning, therefore this study explains the role of machine learning in bridging the semantic gap in content-based image retrieval, proposes an automatic image annotation framework, in which training images are obtained from social media, and semantic indexing is achieved using a combination of supervised and unsupervised machine learning. Furthermore, the study also highlights the need for continuous vocabulary improvement for optimum system performance and recommends hardware implementation of machine learning algorithms to ensure high overall speed of image retrieval systems.

2015 Signal Processing Symposium (SPSympo), 2015

Unsupervised image classification is the process by which each image in a dataset is identified t... more Unsupervised image classification is the process by which each image in a dataset is identified to be a member of one of the inherent categories present in the image collection without the use of labelled training samples. Unsupervised categorisation of images relies on unsupervised machine learning algorithms for its implementation. This paper identifies clustering algorithms and dimension reduction algorithms as the two main classes of unsupervised machine learning algorithms needed in unsupervised image categorisation, and then reviews how these algorithms are used in some notable implementation of unsupervised image classification algorithms.

University of Wollongong, 2020

In recent times, the ability to efficiently manage a large number of images is an important requi... more In recent times, the ability to efficiently manage a large number of images is an important requirement of image repositories due to the increasing number of images being generated and stored in systems such as social media, digital libraries, and geographical information systems. Content Based Image Retrieval involves the management of image repositories based on the content

The Institution of Engineering and Technology - Image Processing, 2020

The Bag-of-Visual Words has been recognised as an effective mean of representing images for image... more The Bag-of-Visual Words has been recognised as an effective mean of representing images for image classification. However, its reliance on a visual codebook developed using Hand Crafted image feature extraction algorithms and vector quantisation via k-means clustering often results in significant computational overhead, and poor classification accuracies. Therefore, this paper presents an adaptive Bag-of-Visual Word Modelling in which Image Feature Extraction is achieved using Deep Feature Learning and the amount of computation required for the development of Visual Codebook is minised using a batch implementation of Particle Swarm Optimisation. The proposed method is tested using Caltech 101 image dataset, and the results confirm the suitability of the proposed method in improving the categorisation performance while reducing the computational load.

Computer Science & Information Technology, 2015

The determination of Region-of-Interest has been recognised as an important means by which unimpo... more The determination of Region-of-Interest has been recognised as an important means by which unimportant image content can be identified and excluded during image compression or image modelling, however existing Region-of-Interest detection methods are computationally expensive thus are mostly unsuitable for managing large number of images and the compression of images especially for real-time video applications. This paper therefore proposes an unsupervised algorithm that takes advantage of the high computation speed being offered by Speeded-Up Robust Features (SURF) and Features from Accelerated Segment Test (FAST) to achieve fast and efficient Region-of-Interest detection.

International Journal of Image Processing (IJIP), Volume (8) : Issue (5) , 2014

Unsupervised image classification is the process by which each image in a dataset is identified t... more Unsupervised image classification is the process by which each image in a dataset is identified to be a member of one of the inherent categories present in the image collection without the use of labelled training samples. Unsupervised categorisation of images relies on unsupervised machine learning algorithms for its implementation. This paper identifies clustering algorithms and dimension reduction algorithms as the two main classes of unsupervised machine learning algorithms needed in unsupervised image categorisation, and then reviews how these algorithms are used in some notable implementation of unsupervised image classification algorithms.

The Institution of Engineering and Technology - Image Processing, Jun 20, 2019

The massive amount of digital content generated daily in the modern world has created the need fo... more The massive amount of digital content generated daily in the modern world has created the need for an image retrieval system built on image analysis via image processing and machine learning, therefore this study explains the role of machine learning in bridging the semantic gap in content-based image retrieval, proposes an automatic image annotation framework, in which training images are obtained from social media, and semantic indexing is achieved using a combination of supervised and unsupervised machine learning. Furthermore, the study also highlights the need for continuous vocabulary improvement for optimum system performance and recommends hardware implementation of machine learning algorithms to ensure high overall speed of image retrieval systems.

Conference Presentations by Abass Olaode

International Conference on Computer and Communications, 2019

The Bag-of-Visual Words has been recognised as an effective mean of representing images for image... more The Bag-of-Visual Words has been recognised as an effective mean of representing images for image classification. However, its reliance on hand crafted image feature extraction algorithms often results in significant computational overhead, and poor classification accuracies. Therefore, this paper presents a Bag-of-Visual Word Modelling in which Image Feature Extraction is achieved using Deep Feature Learning via Stacked-Autoencoder. The proposed method is tested using three image collections constituted from the Caltech 101 image collection and the results confirm the ability of deep feature learning to yield optimum image categorisation performance.

11th International Conference on Signal-Image Technology & Internet-Based Systems (SITIS), 2015

The Bag-of-Visual has been recognised as an effective mean of representing images for the purpose... more The Bag-of-Visual has been recognised as an effective mean of representing images for the purpose of image classification. This paper explains that the quality and quantity of visual-words in the Bag-of-Visual Words codebook generated from an image collection should correlate to the diversity of image contents, and proposes a BOVW codebook development approach that uses the elimination of image features spatial redundancy, batch vector quantisation, and the imposition of an image feature similarity threshold function in generating a codebook that considers the content diversity of the image collection to be classified. With the aid of experimental image collections constituted from Caltech-101 Image set, this paper also demonstrates the importance of this codebook development approach in the determination of the suitable number of latent topics for the implementation of image categorisation via Probabilistic Latent Semantic Analysis for the semantic content annotation of images.

International Conference on Digital Image Computing: Techniques and Applications (DICTA), 2014

Image annotation has been identified to be a suitable means by which the semantic gap which has m... more Image annotation has been identified to be a suitable means by which the semantic gap which has made the accuracy of Content-based image retrieval unsatisfactory be eliminated. However existing methods of automatic annotation of images depends on supervised learning, which can be difficult to implement due to the need for manually annotated training samples which are not always readily available. This paper argues that the unsupervised learning via Probabilistic Latent Semantic Analysis provides a more suitable machine learning approach for image annotation especially due to its potential to based categorisation on the latent semantic content of the image samples, which can bridge the semantic gap present in Content Based Image Retrieval. This paper therefore proposes an unsupervised image categorisation model in which the semantic content of images are discovered using Probabilistic Latent Semantic Analysis, after which they are clustered into unique groups based on semantic content similarities using K-means algorithm, thereby providing suitable annotation exemplars. A common problem with categorisation algorithms based on Bag-of-Visual Words modelling is the loss of accuracy due to spatial incoherency of the Bag-of-Visual Word modelling, this paper also examines the effectiveness of Spatial pyramid as a means of eliminating spatial incoherency in Probabilistic Latent Semantic Analysis classification. Keywords-unsupervised image classification; probabilistic latent semantic analysis; k-means clustering; Bag-of-visual Word; spatial incoherency, spatial pyramid

International Conference on Image and Vision Computing New Zealand (IVCNZ), 2018

The Bag-of-Visual Word modelling of images has been recognised as an important step in the catego... more The Bag-of-Visual Word modelling of images has been recognised as an important step in the categorisation of images for Image retrieval due to its ability to support image semantic content detection. However, its application often leads to significant misclassifications due to its neglecting of the spatial locations of the visual words within the image space during the modelling process. This paper presents Visual sentences constructed via unsupervised Region of interest detection as a viable means of including the spatial locations of visual words in the Bag-of-Visual Word modelling, thereby eliminating the spatial incoherency often associated with the Bag-of-Visual Word Modelling.

Signal Processing Symposium (SPSympo), 2015

The determination of Region-of-Interest can be used as a means of improving the performance of im... more The determination of Region-of-Interest can be used as a means of improving the performance of image retrieval, when used in image annotation as a step in the indexing of images collection. It also has the potential to support efficient video compression for real-time applications. However, existing Region-of-Interest detection methods are mostly unsuitable for managing large number of images and for real-time video applications due to their high computational requirements. This paper therefore proposes an unsupervised algorithm which applies blind image division in the determination of relevant regions within an image space.

2021 10th International Conference on Software and Computer Applications, 2021

A smart wheelchair requires a security system for its users to feel safe and comfortable. The pro... more A smart wheelchair requires a security system for its users to feel safe and comfortable. The process of observing road conditions is one of the solutions to maintaining user safety, which one of these hurdles can be a sudden transition of the situation in surface road height level for example, such as a descending staircase. Integration system for safety in smart wheelchairs consists of three main parts, namely input (Camera), output (Driver motor Left and Right), and main processing (Mini PC). The proposed research will be carried out stair decent detection using a Gray Level Co-occurrence matrix (GLCM) algorithm method as an extraction feature algorithm. The usage of GLCM methods can be applied to images that have textures. While if we look at the descent of the stairs also has a different texture when compared to the usual floor. Support Vector Machine (SVM) is used for the classification of stairs descent and floor. SVM algorithms have advantageous in it is effortless and strong consistency of implementation in classification. In this research propose combination methods between the texture features using GLCM and classification method using SVM to obtain effective detection stairs descent and floor.The proposed method by setting the GLCM parameter with a value of d = 1 and θ = 135o, and SVM classification using the Radial Basis Function Kernel (RBF Kernel) has an accuracy of 87 for detecting the stair descent with relatively fast computation time equal to 0.007 second.

IET Image Processing

The massive amount of digital content generated daily in the modern world has created the need fo... more The massive amount of digital content generated daily in the modern world has created the need for an image retrieval system built on image analysis via image processing and machine learning, therefore this study explains the role of machine learning in bridging the semantic gap in content-based image retrieval, proposes an automatic image annotation framework, in which training images are obtained from social media, and semantic indexing is achieved using a combination of supervised and unsupervised machine learning. Furthermore, the study also highlights the need for continuous vocabulary improvement for optimum system performance and recommends hardware implementation of machine learning algorithms to ensure high overall speed of image retrieval systems.

2015 Signal Processing Symposium (SPSympo), 2015

Unsupervised image classification is the process by which each image in a dataset is identified t... more Unsupervised image classification is the process by which each image in a dataset is identified to be a member of one of the inherent categories present in the image collection without the use of labelled training samples. Unsupervised categorisation of images relies on unsupervised machine learning algorithms for its implementation. This paper identifies clustering algorithms and dimension reduction algorithms as the two main classes of unsupervised machine learning algorithms needed in unsupervised image categorisation, and then reviews how these algorithms are used in some notable implementation of unsupervised image classification algorithms.

University of Wollongong, 2020

In recent times, the ability to efficiently manage a large number of images is an important requi... more In recent times, the ability to efficiently manage a large number of images is an important requirement of image repositories due to the increasing number of images being generated and stored in systems such as social media, digital libraries, and geographical information systems. Content Based Image Retrieval involves the management of image repositories based on the content

The Institution of Engineering and Technology - Image Processing, 2020

The Bag-of-Visual Words has been recognised as an effective mean of representing images for image... more The Bag-of-Visual Words has been recognised as an effective mean of representing images for image classification. However, its reliance on a visual codebook developed using Hand Crafted image feature extraction algorithms and vector quantisation via k-means clustering often results in significant computational overhead, and poor classification accuracies. Therefore, this paper presents an adaptive Bag-of-Visual Word Modelling in which Image Feature Extraction is achieved using Deep Feature Learning and the amount of computation required for the development of Visual Codebook is minised using a batch implementation of Particle Swarm Optimisation. The proposed method is tested using Caltech 101 image dataset, and the results confirm the suitability of the proposed method in improving the categorisation performance while reducing the computational load.

Computer Science & Information Technology, 2015

The determination of Region-of-Interest has been recognised as an important means by which unimpo... more The determination of Region-of-Interest has been recognised as an important means by which unimportant image content can be identified and excluded during image compression or image modelling, however existing Region-of-Interest detection methods are computationally expensive thus are mostly unsuitable for managing large number of images and the compression of images especially for real-time video applications. This paper therefore proposes an unsupervised algorithm that takes advantage of the high computation speed being offered by Speeded-Up Robust Features (SURF) and Features from Accelerated Segment Test (FAST) to achieve fast and efficient Region-of-Interest detection.

International Journal of Image Processing (IJIP), Volume (8) : Issue (5) , 2014

Unsupervised image classification is the process by which each image in a dataset is identified t... more Unsupervised image classification is the process by which each image in a dataset is identified to be a member of one of the inherent categories present in the image collection without the use of labelled training samples. Unsupervised categorisation of images relies on unsupervised machine learning algorithms for its implementation. This paper identifies clustering algorithms and dimension reduction algorithms as the two main classes of unsupervised machine learning algorithms needed in unsupervised image categorisation, and then reviews how these algorithms are used in some notable implementation of unsupervised image classification algorithms.

The Institution of Engineering and Technology - Image Processing, Jun 20, 2019

The massive amount of digital content generated daily in the modern world has created the need fo... more The massive amount of digital content generated daily in the modern world has created the need for an image retrieval system built on image analysis via image processing and machine learning, therefore this study explains the role of machine learning in bridging the semantic gap in content-based image retrieval, proposes an automatic image annotation framework, in which training images are obtained from social media, and semantic indexing is achieved using a combination of supervised and unsupervised machine learning. Furthermore, the study also highlights the need for continuous vocabulary improvement for optimum system performance and recommends hardware implementation of machine learning algorithms to ensure high overall speed of image retrieval systems.

International Conference on Computer and Communications, 2019

The Bag-of-Visual Words has been recognised as an effective mean of representing images for image... more The Bag-of-Visual Words has been recognised as an effective mean of representing images for image classification. However, its reliance on hand crafted image feature extraction algorithms often results in significant computational overhead, and poor classification accuracies. Therefore, this paper presents a Bag-of-Visual Word Modelling in which Image Feature Extraction is achieved using Deep Feature Learning via Stacked-Autoencoder. The proposed method is tested using three image collections constituted from the Caltech 101 image collection and the results confirm the ability of deep feature learning to yield optimum image categorisation performance.

11th International Conference on Signal-Image Technology & Internet-Based Systems (SITIS), 2015

The Bag-of-Visual has been recognised as an effective mean of representing images for the purpose... more The Bag-of-Visual has been recognised as an effective mean of representing images for the purpose of image classification. This paper explains that the quality and quantity of visual-words in the Bag-of-Visual Words codebook generated from an image collection should correlate to the diversity of image contents, and proposes a BOVW codebook development approach that uses the elimination of image features spatial redundancy, batch vector quantisation, and the imposition of an image feature similarity threshold function in generating a codebook that considers the content diversity of the image collection to be classified. With the aid of experimental image collections constituted from Caltech-101 Image set, this paper also demonstrates the importance of this codebook development approach in the determination of the suitable number of latent topics for the implementation of image categorisation via Probabilistic Latent Semantic Analysis for the semantic content annotation of images.

International Conference on Digital Image Computing: Techniques and Applications (DICTA), 2014

Image annotation has been identified to be a suitable means by which the semantic gap which has m... more Image annotation has been identified to be a suitable means by which the semantic gap which has made the accuracy of Content-based image retrieval unsatisfactory be eliminated. However existing methods of automatic annotation of images depends on supervised learning, which can be difficult to implement due to the need for manually annotated training samples which are not always readily available. This paper argues that the unsupervised learning via Probabilistic Latent Semantic Analysis provides a more suitable machine learning approach for image annotation especially due to its potential to based categorisation on the latent semantic content of the image samples, which can bridge the semantic gap present in Content Based Image Retrieval. This paper therefore proposes an unsupervised image categorisation model in which the semantic content of images are discovered using Probabilistic Latent Semantic Analysis, after which they are clustered into unique groups based on semantic content similarities using K-means algorithm, thereby providing suitable annotation exemplars. A common problem with categorisation algorithms based on Bag-of-Visual Words modelling is the loss of accuracy due to spatial incoherency of the Bag-of-Visual Word modelling, this paper also examines the effectiveness of Spatial pyramid as a means of eliminating spatial incoherency in Probabilistic Latent Semantic Analysis classification. Keywords-unsupervised image classification; probabilistic latent semantic analysis; k-means clustering; Bag-of-visual Word; spatial incoherency, spatial pyramid

International Conference on Image and Vision Computing New Zealand (IVCNZ), 2018

The Bag-of-Visual Word modelling of images has been recognised as an important step in the catego... more The Bag-of-Visual Word modelling of images has been recognised as an important step in the categorisation of images for Image retrieval due to its ability to support image semantic content detection. However, its application often leads to significant misclassifications due to its neglecting of the spatial locations of the visual words within the image space during the modelling process. This paper presents Visual sentences constructed via unsupervised Region of interest detection as a viable means of including the spatial locations of visual words in the Bag-of-Visual Word modelling, thereby eliminating the spatial incoherency often associated with the Bag-of-Visual Word Modelling.

Signal Processing Symposium (SPSympo), 2015

The determination of Region-of-Interest can be used as a means of improving the performance of im... more The determination of Region-of-Interest can be used as a means of improving the performance of image retrieval, when used in image annotation as a step in the indexing of images collection. It also has the potential to support efficient video compression for real-time applications. However, existing Region-of-Interest detection methods are mostly unsuitable for managing large number of images and for real-time video applications due to their high computational requirements. This paper therefore proposes an unsupervised algorithm which applies blind image division in the determination of relevant regions within an image space.