Abass A. Olaode

Bio: Abass A. Olaode is an academic researcher from University of Wollongong. The author has contributed to research in topics: Image retrieval & Automatic image annotation. The author has an hindex of 4, co-authored 10 publications receiving 64 citations.

Unsupervised Classification of Images: A Review

Abstract: 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.

Unsupervised Image Classification by Probabilistic Latent Semantic Analysis for the Annotation of Images

Abstract: -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.

Efficient region of interest detection using blind image division

Abstract: 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.

Bag-of-Visual Words Codebook Development for the Semantic Content Based Annotation of Images

Abstract: 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.

Review of the application of machine learning to the automatic semantic annotation of images

Abstract: 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.

Sentinel-2 Data for Land Cover/Use Mapping: A Review

Abstract: The advancement in satellite remote sensing technology has revolutionised the approaches to monitoring the Earth’s surface. The development of the Copernicus Programme by the European Space Agency (ESA) and the European Union (EU) has contributed to the effective monitoring of the Earth’s surface by producing the Sentinel-2 multispectral products. Sentinel-2 satellites are the second constellation of the ESA Sentinel missions and carry onboard multispectral scanners. The primary objective of the Sentinel-2 mission is to provide high resolution satellite data for land cover/use monitoring, climate change and disaster monitoring, as well as complementing the other satellite missions such as Landsat. Since the launch of Sentinel-2 multispectral instruments in 2015, there have been many studies on land cover/use classification which use Sentinel-2 images. However, no review studies have been dedicated to the application of ESA Sentinel-2 land cover/use monitoring. Therefore, this review focuses on two aspects: (1) assessing the contribution of ESA Sentinel-2 to land cover/use classification, and (2) exploring the performance of Sentinel-2 data in different applications (e.g., forest, urban area and natural hazard monitoring). The present review shows that Sentinel-2 has a positive impact on land cover/use monitoring, specifically in monitoring of crop, forests, urban areas, and water resources. The contemporary high adoption and application of Sentinel-2 can be attributed to the higher spatial resolution (10 m) than other medium spatial resolution images, the high temporal resolution of 5 days and the availability of the red-edge bands with multiple applications. The ability to integrate Sentinel-2 data with other remotely sensed data, as part of data analysis, improves the overall accuracy (OA) when working with Sentinel-2 images. The free access policy drives the increasing use of Sentinel-2 data, especially in developing countries where financial resources for the acquisition of remotely sensed data are limited. The literature also shows that the use of Sentinel-2 data produces high accuracies (>80%) with machine-learning classifiers such as support vector machine (SVM) and Random forest (RF). However, other classifiers such as maximum likelihood analysis are also common. Although Sentinel-2 offers many opportunities for land cover/use classification, there are challenges which include mismatching with Landsat OLI-8 data, a lack of thermal bands, and the differences in spatial resolution among the bands of Sentinel-2. Sentinel-2 data show promise and have the potential to contribute significantly towards land cover/use monitoring.

Urban Growth Dynamics and Changing Land-Use Land-Cover of Megacity Kolkata and Its Environs

Abstract: Spatio-temporal land-use land-cover changes have a long-term impact on urban environments. The present study is based on land-use land-cover changes and urban expansion of megacity Kolkata and its environs over three decades (1991–2018) using multitemporal Landsat data. The study aims to explore and explain the spatio-temporal land-use land-cover change, areal differentiation, spatio-temporal urban growth trajectory and future land-use land-cover prediction with population projection. The spatio-temporal representation found rapid urbanization, i.e. 19% to 57%, exactly three times as in 1991, resulting in significant loss of other than urban/built-up area. Urban trajectory reveals that the expansion mainly occurred in north-east to south-west direction, the zone of both sides of River Hooghly. Areal differentiation map with highest urbanization (3146 ha or UII = 0.64) was identified in the north–north-west part, while least urbanization was identified in the east–north-east direction. On the other hand, this urbanization has grabbed most (i.e. 87%) of the areas within 5-km ring buffer compared to other three ring buffers. Being Kolkata as a traditional city, it has all modern facilities since British rule; as a result, the high population growth and rapid urban expansion were explored in the study. Therefore, urban growth led to radical changes in land-use land-cover, which were witnessed by sharp decreases in sparse vegetation and fallow land. The correlation explained that increasing urbanization has decreased the amount of water body and vegetation. The future prediction graph identified the more horrible picture: the city and its environs will be covered by 67% built-up, while there will be only 3% water body, 14% vegetation and 16% fallow land of the total geographical area with a population (projection) of 28 million in 2051 if it is continued. Such expansion will create a wide range of mismanagement and environmental problems. Hence, the intensive explanation and areal differentiation maps and diagrams prepared using geospatial data will definitely help to understand the urban growth dynamics process and changing form of land-use land-cover and simultaneously decision-making process of the local planners, stakeholders and academicians. Therefore, it also guides to future planning to decrease the adverse effects of urbanization and result in the form of land-use land-cover and makes an eco-friendly megacity as well as sustainable urban development too.

Image Based Artificial Intelligence in Wound Assessment: A Systematic Review

Abstract: Efficient and effective assessment of acute and chronic wounds can help wound care teams in clinical practice to greatly improve wound diagnosis, optimize treatment plans, ease the workload and achieve health related quality of life to the patient population. While artificial intelligence (AI) has found wide applications in health-related sciences and technology, AI-based systems remain to be developed clinically and computationally for high-quality wound care. To this end, we have carried out a systematic review of intelligent image-based data analysis and system developments for wound assessment. Specifically, we provide an extensive review of research methods on wound measurement (segmentation) and wound diagnosis (classification). We also reviewed recent work on wound assessment systems (including hardware, software, and mobile apps). More than 250 articles were retrieved from various publication databases and online resources, and 115 of them were carefully selected to cover the breadth and depth of most recent and relevant work to convey the current review to its fulfillment.

K-Means Clustering of Ambient Air Quality Data of Uttarakhand, India during Lockdown Period of Covid-19 Pandemic

Abstract: The analysis of the lockdown effect during covid-19 pandemic on ambient quality of air of Uttarakhand state of India, has been performed. The combination of SO 2 , NO 2 , and particulate matter (P.M.10) indicates ambient air quality characteristics. The clustering capability of the K-means clustering technique is investigated with two different approaches of measuring distance using MATLAB. The first approach is termed Euclidean distance and the second one is cosine distance. The data, which is clustered, is the air uualitv data containing three major components of air pollution such as P.M.10, SO 2 , and NO 2 of different major cities of Uttarakhand.