IEEE Transactions on Knowledge and Data Engineering, 2004
We present a new framework for novelty detection. The framework evaluates neural networks as adap... more We present a new framework for novelty detection. The framework evaluates neural networks as adaptive classifiers that are capable of novelty detection and retraining on the basis of newly discovered information. We apply our newly developed model to the application area of object recognition in video. We detail the tools and methods needed for novelty detection such that data from unknown classes can be reliably rejected without any a priori knowledge of its characteristics. The rejected data is postprocessed to determine which samples can be manually labeled of a new type and used for retraining. We compare the proposed framework with other novelty detection methods and discuss the results of adaptive retraining of neural network to recognize further unseen data containing the newly added objects.
EVALUATION OF TEXTURE METHODS FOR IMAGE ANALYSIS Mona Sharma, Sameer Singh* {Email: m.sharma, s.s... more EVALUATION OF TEXTURE METHODS FOR IMAGE ANALYSIS Mona Sharma, Sameer Singh* {Email: m.sharma, s.singh}@exeter.ac.uk PANN Research Department of Computer Science University of Exeter Exeter EX4 4PT United Kingdom ABSTRACT The evaluation of ...
Abstract Novelty detection is the identification of new or unknown data or signal that a machine ... more Abstract Novelty detection is the identification of new or unknown data or signal that a machine learning system is not aware of during training Novelty detection is one of the fundamental requirements of a good classification or identification system since sometimes the test data contains information about objects that were not known at the time of training the model In
This paper proposes a new technique for the classification of indoor and outdoor images based on ... more This paper proposes a new technique for the classification of indoor and outdoor images based on edge analysis. Our technique is based on analysing edge straightness in images. We make an original proposal that indoor images have a greater proportion of edges that are ...
IEEE Transactions on Information Technology in Biomedicine, 2005
The main aim of this paper is to propose a novel set of metrics that measure the quality of the i... more The main aim of this paper is to propose a novel set of metrics that measure the quality of the image enhancement of mammographic images in a computer-aided detection framework aimed at automatically finding masses using machine learning techniques. Our methodology includes a novel mechanism for the combination of the metrics proposed into a single quantitative measure. We have evaluated our methodology on 200 images from the publicly available digital database for screening mammograms. We show that the quantitative measures help us select the best suited image enhancement on a per mammogram basis, which improves the quality of subsequent image segmentation much better than using the same enhancement method for all mammograms.
Abstract. In this paper we investigate a new approach to the classification of mammographic image... more Abstract. In this paper we investigate a new approach to the classification of mammographic images according to breast type. The classification of breast density in this study is motivated by its use as prior knowledge in the image processing pipeline. By utilising this ...
IEEE Transactions on Knowledge and Data Engineering, 2004
We present a new framework for novelty detection. The framework evaluates neural networks as adap... more We present a new framework for novelty detection. The framework evaluates neural networks as adaptive classifiers that are capable of novelty detection and retraining on the basis of newly discovered information. We apply our newly developed model to the application area of object recognition in video. We detail the tools and methods needed for novelty detection such that data from unknown classes can be reliably rejected without any a priori knowledge of its characteristics. The rejected data is postprocessed to determine which samples can be manually labeled of a new type and used for retraining. We compare the proposed framework with other novelty detection methods and discuss the results of adaptive retraining of neural network to recognize further unseen data containing the newly added objects.
EVALUATION OF TEXTURE METHODS FOR IMAGE ANALYSIS Mona Sharma, Sameer Singh* {Email: m.sharma, s.s... more EVALUATION OF TEXTURE METHODS FOR IMAGE ANALYSIS Mona Sharma, Sameer Singh* {Email: m.sharma, s.singh}@exeter.ac.uk PANN Research Department of Computer Science University of Exeter Exeter EX4 4PT United Kingdom ABSTRACT The evaluation of ...
Abstract Novelty detection is the identification of new or unknown data or signal that a machine ... more Abstract Novelty detection is the identification of new or unknown data or signal that a machine learning system is not aware of during training Novelty detection is one of the fundamental requirements of a good classification or identification system since sometimes the test data contains information about objects that were not known at the time of training the model In
This paper proposes a new technique for the classification of indoor and outdoor images based on ... more This paper proposes a new technique for the classification of indoor and outdoor images based on edge analysis. Our technique is based on analysing edge straightness in images. We make an original proposal that indoor images have a greater proportion of edges that are ...
IEEE Transactions on Information Technology in Biomedicine, 2005
The main aim of this paper is to propose a novel set of metrics that measure the quality of the i... more The main aim of this paper is to propose a novel set of metrics that measure the quality of the image enhancement of mammographic images in a computer-aided detection framework aimed at automatically finding masses using machine learning techniques. Our methodology includes a novel mechanism for the combination of the metrics proposed into a single quantitative measure. We have evaluated our methodology on 200 images from the publicly available digital database for screening mammograms. We show that the quantitative measures help us select the best suited image enhancement on a per mammogram basis, which improves the quality of subsequent image segmentation much better than using the same enhancement method for all mammograms.
Abstract. In this paper we investigate a new approach to the classification of mammographic image... more Abstract. In this paper we investigate a new approach to the classification of mammographic images according to breast type. The classification of breast density in this study is motivated by its use as prior knowledge in the image processing pipeline. By utilising this ...
Uploads
Papers by sameer singh