Fast embedding for image classification & retrieval and its application to the hostel industry

This thesis was submitted for the award of Doctor of Philosophy and was awarded by Brunel University LondonContent-based image classification and retrieval are the automatic processes of taking an unseen image input and extracting its features representing the input image. Then, for the classification task, this mathematically measured input is categorized according to established criteria in the server and consequently shows the output as a result. On the other hand, for the retrieval task, the extracted features of an unseen query image are sent to the server to search for the most visually similar images to a given image and retrieve these images as a result. Despite image features could be represented by classical features, artificial intelligence-based features, Convolutional Neural Networks (CNN) to be precise, have become powerful tools in the field. Nonetheless, the high dimensional CNN features have been a challenge in particular for applications on mobile or Internet of Things devices. Therefore, in this thesis, several fast embeddings are explored and proposed to overcome the constraints of low memory, bandwidth, and power. Furthermore, the first hostel image database is created with three datasets, hostel image dataset containing 13,908 interior and exterior images of hostels across the world, and Hostels-900 dataset and Hostels-2K dataset containing 972 images and 2,380 images, respectively, of 20 London hostel buildings. The results demonstrate that the proposed fast embeddings such as the application of GHM-Rand operator, GHM-Fix operator, and binary feature vectors are able to outperform or give competitive results to those state-of-the-art methods with a lot less computational resource. Additionally, the findings from a ten-year literature review of CBIR study in the tourism industry could picturize the relevant research activities in the past decade which are not only beneficial to the hostel industry or tourism sector but also to the computer science and engineering research communities for the potential real-life applications of the existing and developing technologies in the field

Computer-Assisted Diagnosis System in Digestive Endoscopy

International audienceThe purpose of this paper is to present an intelligent atlas of indexed endoscopic lesions that could be used in com- puter-assisted diagnosis as reference data. The development of such a system requires a mix of medical and engineering skills for analyzing and reproducing the cognitive processes that un- derlie the medical decision-making process. The analysis of both endoscopists experience and endoscopic terminologies developed by professional associations shows that diagnostic reasoning in digestive endoscopy uses a scene-object approach. The objects correspond to the endoscopic findings and the medical context of examination and the scene to the endoscopic diagnosis. According to expert assessment, the classes of endoscopic findings and diagnoses, their primitive characteristics (or indices), and their relationships have been listed. Each class describes an endoscopic finding or diagnosis in an intensive way. The retrieval method is based on a similarity metric that estimates the membership value of the case under investigation and the prototype of the class. A simulation test with randomized objects demonstrates a good classification of endoscopic findings. The correct class is the unique response in 68% of the tested objects, the first of multiple responses in 28%. Four descriptors are shown to be of major im- portance in the classification algorithm: anatomic location, shape, color, and relief. At the present time, the application database contains approximately 150 endoscopic images and is accessible via Internet. Experiments are in progress with endoscopists for the validation of the system and for the understanding of the similarity between images. The next step will integrate the system in a learning tool for junior endoscopists