Dr Syed Sameed Husain
About
Biography
I am currently a Senior Research Fellow at the Centre for Vision, Speech and Signal Processing (CVSSP), Faculty of Engineering and Physical Sciences, University of Surrey. I am an experienced researcher with a proven track record of world-class research in computer vision and generating strong impact via successful projects with industry (InnovateUK RetinaScan, iTravel, CODAM and H2020 BRIDGET). My scientific contributions have been recognized by several global awards: Gold Medals in Google Landmark Retrieval Challenge 2018 and Google YouTube 8M Video Understanding Challenge 2018. My research focuses on novel techniques in machine learning and computer vision and their applications in industry, healthcare and security. I have a particular interest in image and video recognition. The research aim is to develop robust methods and technologies for visual content understanding that can improve on existing state-of-the art leading to new applications. Since joining CVSSP in 2016 as a Research fellow, I have made considerable contributions to research projects, which, apart from generating novel research, has also involved taking the lead role in communication between partners and funding bodies at both national and international level. My recent work on image recognition, provides a state-of-the-art solution and was recently accepted for publication at IEEE TPAMI. I have also made important contributions to the ISO MPEG standard with technical submissions to ISO and BSI. Importantly, my methods for large scale image retrieval and video classification achieved Gold Medal in prestigious Google competitions. Due to my outstanding research, state-of-the-art results and international awards, I was given Innovator of the year award 2018, by CVSSP.
ResearchResearch interests
My research focuses on novel techniques in machine learning and computer vision and their applications in industry, healthcare and security. I have a particular interest in image and video recognition. The research aim is to develop robust methods and technologies for visual content understanding that can improve on existing state-of-the art leading to new applications. Since joining CVSSP in 2016 as a Research fellow, I have made considerable contributions to research projects, which, apart from generating novel research, has also involved taking the lead role in communication between partners and funding bodies at both national and international level. My recent work on image recognition, provides a state-of-the-art solution and was recently accepted for publication at IEEE TPAMI. I have also made important contributions to the ISO MPEG standard with technical submissions to ISO and BSI. Importantly, my methods for large scale image retrieval and video classification achieved Gold Medal in prestigious Google competitions. Due to my outstanding research, state-of-the-art results and international awards, I was given Innovator of the year award 2018, by CVSSP.
During the first year of my postdoctoral research, I worked on European Commission funded (FP7) project, Bridging the Gap for Enhanced broadcast (BRIDGET). I was responsible for the development of large-scale visual search algorithms for the broadcast industry. My research led to the development of a novel method for deriving a compact and distinctive representation of image content called Robust Visual Descriptor (RVD). It significantly advanced the state-of-the-art and delivered world-class performance. The University of Surrey filed a US patent based on RVD representation for visual search. My work was also published in IEEE Transactions on Pattern Analysis and Machine Intelligence with an impact factor of 17.
During the second year of research, I worked on Innovate UK project Content-based Digital Asset Management (CODAM), to develop an advanced video classification system. As part of the project, our team participated in the Google YouTube 8M Video Understanding Challenge 2018. The task set by Google was to develop algorithms which accurately and automatically assign labels to videos using a dataset created from over seven million YouTube videos. We developed a deep-learning based AI system which can learn to understand the story behind any video and give a short verbal summary of what it is about. Our system won the Google Gold Medal from the 650 entries worldwide. The deep learning system we developed is being used by the BBC and the Metropolitan Police. The work on video classification was published in IEEE Transactions on Circuits and Systems for Video Technology (impact factor 4.06).
In the third year, I was part of the InnovateUK project iTravel where the aim was to develop a smartphone-based intelligent "virtual journey assistant", providing end-to-end routing with proactive contextual information to the traveler, including real-time visual recognition through the smartphone's camera. During the project, our team took part in the Google Landmark Retrieval Challenge 2018. The challenge was to develop the world’s most accurate technology to automatically identify landmarks and retrieve relevant photographs from a database. Our deep learning-based system “REMAP: Multi-layer entropy-guided pooling of dense CNN features for image retrieval” won the prestigious competition, significantly outperforming well known international corporations and global university groups including Google, Layer6 AI Canada, Facebook, Naver Labs Europe, Stanford USA and MIT USA. Our winning solution was published in IEEE Transactions on Image Processing (impact factor 6.8). Furthermore, my recent image recognition system “ACTNET: end-to-end learning of feature activations and aggregation for effective instance image retrieval” won Gold Medal in Google Landmark Retrieval Challenge 2019 beating competition from 300 teams.
Currently, I am working on InnovateUK project “RetinaScan: AI-enabled automated image assessment system for diabetic retinopathy screening”, in close collaboration with the NHS. Diabetic Retinopathy (DR) is a major cause of blindness but easily ameliorated through laser or drug treatment. Annual routine screening enables DR to be captured and treated early. While diabetic eye screening system are highly effective, they are labor intensive, slow and expensive. To solve these problems, we developed a deep learning system for detection of diabetic retinopathy and segmentation of retinal lesions associated with diabetic retinopathy. My goal is to continue improving the sensitivity and specificity for detection referable diabetic retinopathy. Furthermore, significant research is necessary to determine the feasibility of applying this algorithm in the clinical setting and to determine whether use of the algorithm could lead to improved care and outcomes compared with current ophthalmologic assessment.
Research interests
My research focuses on novel techniques in machine learning and computer vision and their applications in industry, healthcare and security. I have a particular interest in image and video recognition. The research aim is to develop robust methods and technologies for visual content understanding that can improve on existing state-of-the art leading to new applications. Since joining CVSSP in 2016 as a Research fellow, I have made considerable contributions to research projects, which, apart from generating novel research, has also involved taking the lead role in communication between partners and funding bodies at both national and international level. My recent work on image recognition, provides a state-of-the-art solution and was recently accepted for publication at IEEE TPAMI. I have also made important contributions to the ISO MPEG standard with technical submissions to ISO and BSI. Importantly, my methods for large scale image retrieval and video classification achieved Gold Medal in prestigious Google competitions. Due to my outstanding research, state-of-the-art results and international awards, I was given Innovator of the year award 2018, by CVSSP.
During the first year of my postdoctoral research, I worked on European Commission funded (FP7) project, Bridging the Gap for Enhanced broadcast (BRIDGET). I was responsible for the development of large-scale visual search algorithms for the broadcast industry. My research led to the development of a novel method for deriving a compact and distinctive representation of image content called Robust Visual Descriptor (RVD). It significantly advanced the state-of-the-art and delivered world-class performance. The University of Surrey filed a US patent based on RVD representation for visual search. My work was also published in IEEE Transactions on Pattern Analysis and Machine Intelligence with an impact factor of 17.
During the second year of research, I worked on Innovate UK project Content-based Digital Asset Management (CODAM), to develop an advanced video classification system. As part of the project, our team participated in the Google YouTube 8M Video Understanding Challenge 2018. The task set by Google was to develop algorithms which accurately and automatically assign labels to videos using a dataset created from over seven million YouTube videos. We developed a deep-learning based AI system which can learn to understand the story behind any video and give a short verbal summary of what it is about. Our system won the Google Gold Medal from the 650 entries worldwide. The deep learning system we developed is being used by the BBC and the Metropolitan Police. The work on video classification was published in IEEE Transactions on Circuits and Systems for Video Technology (impact factor 4.06).
In the third year, I was part of the InnovateUK project iTravel where the aim was to develop a smartphone-based intelligent "virtual journey assistant", providing end-to-end routing with proactive contextual information to the traveler, including real-time visual recognition through the smartphone's camera. During the project, our team took part in the Google Landmark Retrieval Challenge 2018. The challenge was to develop the world’s most accurate technology to automatically identify landmarks and retrieve relevant photographs from a database. Our deep learning-based system “REMAP: Multi-layer entropy-guided pooling of dense CNN features for image retrieval” won the prestigious competition, significantly outperforming well known international corporations and global university groups including Google, Layer6 AI Canada, Facebook, Naver Labs Europe, Stanford USA and MIT USA. Our winning solution was published in IEEE Transactions on Image Processing (impact factor 6.8). Furthermore, my recent image recognition system “ACTNET: end-to-end learning of feature activations and aggregation for effective instance image retrieval” won Gold Medal in Google Landmark Retrieval Challenge 2019 beating competition from 300 teams.
Currently, I am working on InnovateUK project “RetinaScan: AI-enabled automated image assessment system for diabetic retinopathy screening”, in close collaboration with the NHS. Diabetic Retinopathy (DR) is a major cause of blindness but easily ameliorated through laser or drug treatment. Annual routine screening enables DR to be captured and treated early. While diabetic eye screening system are highly effective, they are labor intensive, slow and expensive. To solve these problems, we developed a deep learning system for detection of diabetic retinopathy and segmentation of retinal lesions associated with diabetic retinopathy. My goal is to continue improving the sensitivity and specificity for detection referable diabetic retinopathy. Furthermore, significant research is necessary to determine the feasibility of applying this algorithm in the clinical setting and to determine whether use of the algorithm could lead to improved care and outcomes compared with current ophthalmologic assessment.