Markos Markou

Novelty detection: a review—part 1: statistical approaches

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 we provide state-of-the-art review in the area of novelty detection based on statistical approaches. The second part paper details novelty detection using neural networks. As discussed, there are a multitude of applications where novelty detection is extremely important including signal processing, computer vision, pattern recognition, data mining, and robotics.

Novelty detection: a review—part 2: neural network based approaches

Novelty detection is the identification of new or unknown data or signal that a machine learning system is not aware of during training. In this paper we focus on neural network-based approaches for novelty detection. Statistical approaches are covered in Part 1 paper.

An approach to novelty detection applied to the classification of image regions

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.

Colour image texture analysis: dependence on colour spaces

In this paper we investigate the role of colour spaces on texture analysis. We extract a range of correlogram and colour moment features for the VisTex colour texture benchmark in different colour spaces and find the average probabilistic distance of separation across different objects for different features and suggest the colour spaces that are best suited for the classification process. We also show the results of k-nearest neighbour classification for different features and their combined set.

Feature selection for face recognition based on data partitioning

Feature selection is an important consideration in several applications where one needs to choose a smaller subset of features from a complete set of raw measurements such that the improved subset generates as good or better classification performance compared to original data. In this paper, we describe a novel feature selection approach that is based on the estimation of classification complexity through data partitioning. This approach allows us to select the N best features from a given set in an order of their ability to separate data from different classes. In this paper, we perform our experiments on the ORL face database that consists of 400 images. The results show that the proposed approach outperforms the probability distance approach and is a viable method for implementing more advanced search methods of feature selection.

Natural object classification using artificial neural networks

In this paper we apply artificial neural networks for classifying texture data of various natural objects found in FLIR images. Hermite functions are used for texture feature extraction from segmented regions of interest in natural scenes taken as a video sequence. A total of 2680 samples for a total of twelve different classes are used for object recognition. The results on correctly identifying twelve natural objects in scenes are compared across ten folds of the cross-validation study. Neural networks are found to be extremely effective in robust classification of our data giving an average recognition rate of 91.8%.

Detection of new image objects in video sequences using neural networks

The detection of image segmented objects in video sequences is constrained by the a priori information available with a classifier. An object recognizer labels image regions based on texture and shape information about objects for which historical data is available. The introduction of a new object would culminate in its misclassification as the closest possible object known to the recognizer. Neural networks can be used to develop a strategy to automatically recognize new objects in image scenes that can be separated from other data for manual labeling. In this paper, one such strategy is presented for natural scene analysis of FLIR images. Appropriate threshold tests for classification are developed for separating known from unknown information. The results show that very high success rates can be obtained using neural networks for the labeling of new objects in scene analysis.

Neural network analysis of MINERVA scene analysis benchmark

Scene analysis is an important area of research with the aim of identifying objects and their relationships in natural scenes. The MINERVA benchmark has recently been introduced in this area for testing different image processing and classification schemes. We present results on the classification of eight natural objects in the complete set of 448 natural images using neural networks. An exhaustive set of experiments with this benchmark has been conducted using four different segmentation methods and five texture-based feature extraction methods. The results in this paper show the performance of a neural network classifier on a ten fold cross-validation task. On the basis of the results produced, we are able to rank how well different image segmentation algorithms are suited to the task of region of interest identification in these images, and we also see how well texture extraction algorithms rank on the basis of classification results.

Feature selection based on a black hole model of data reorganization

This work introduces a new model of feature selection based on a pattern recognition model using the concept of black holes. We show that this method of feature selection is robust and provides an efficient subset of features for classification.

FLIR image segmentation and natural object classification

In this paper we compare four classification techniques for classifying texture data of various natural objects found in forward-looking infrared (FLIR) images. The techniques compared include linear discriminant analysis, mean classifier and two different models of k-nearest neighbour methods. Hermite functions are used for texture feature extraction from segmented regions of interest in natural scenes taken as a video sequence. A total of 2680 samples for a total of twelve different classes are used for object recognition. The results on correctly identifying twelve natural objects in scenes are compared across the four classifiers on both unnormalised and normalised data. On unnormalised data, the average best recognition rate obtained using a ten fold cross-validation is 96.5%, and on unnormalised data it is 86.1% with a single nearest neighbour technique.