Search results for "RECOGNITION"
showing 10 items of 3607 documents
Ensemble Feature Selection Based on the Contextual Merit
2001
Recent research has proved the benefits of using ensembles of classifiers for classification problems. Ensembles constructed by machine learning methods manipulating the training set are used to create diverse sets of accurate classifiers. Different feature selection techniques based on applying different heuristics for generating base classifiers can be adjusted to specific domain characteristics. In this paper we consider and experiment with the contextual feature merit measure as a feature selection heuristic. We use the diversity of an ensemble as evaluation function in our new algorithm with a refinement cycle. We have evaluated our algorithm on seven data sets from UCI. The experiment…
Towards to deep neural network application with limited training data: synthesis of melanoma's diffuse reflectance spectral images
2019
The goal of our study is to train artificial neural networks (ANN) using multispectral images of melanoma. Since the number of multispectral images of melanomas is limited, we offer to synthesize them from multispectral images of benign skin lesions. We used the previously created melanoma diagnostic criterion p'. This criterion is calculated from multispectral images of skin lesions captured under 526nm, 663nm, and 964nm LED illumination. We synthesize these three images from multispectral images of nevus so that the p' map matches the melanoma criteria (the values in the lesion area is >1, respectively). Demonstrated results show that by transforming multispectral images of benign nevus i…
Null Space Based Image Recognition Using Incremental Eigendecomposition
2011
An incremental approach to the discriminative common vector (DCV) method for image recognition is considered. Discriminative projections are tackled in the particular context in which new training data becomes available and learned subspaces may need continuous updating. Starting from incremental eigendecomposition of scatter matrices, an efficient updating rule based on projections and orthogonalization is given. The corresponding algorithm has been empirically assessed and compared to its batch counterpart. The same good properties and performance results of the original method are kept but with a dramatic decrease in the computation needed.
Learning the structure of HMM's through grammatical inference techniques
2002
A technique is described in which all the components of a hidden Markov model are learnt from training speech data. The structure or topology of the model (i.e. the number of states and the actual transitions) is obtained by means of an error-correcting grammatical inference algorithm (ECGI). This structure is then reduced by using an appropriate state pruning criterion. The statistical parameters that are associated with the obtained topology are estimated from the same training data by means of the standard Baum-Welch algorithm. Experimental results showing the applicability of this technique to speech recognition are presented. >
Ensemble Feature Selection Based on Contextual Merit and Correlation Heuristics
2001
Recent research has proven the benefits of using ensembles of classifiers for classification problems. Ensembles of diverse and accurate base classifiers are constructed by machine learning methods manipulating the training sets. One way to manipulate the training set is to use feature selection heuristics generating the base classifiers. In this paper we examine two of them: correlation-based and contextual merit -based heuristics. Both rely on quite similar assumptions concerning heterogeneous classification problems. Experiments are considered on several data sets from UCI Repository. We construct fixed number of base classifiers over selected feature subsets and refine the ensemble iter…
Learning Similarity Scores by Using a Family of Distance Functions in Multiple Feature Spaces
2017
There exist a large number of distance functions that allow one to measure similarity between feature vectors and thus can be used for ranking purposes. When multiple representations of the same object are available, distances in each representation space may be combined to produce a single similarity score. In this paper, we present a method to build such a similarity ranking out of a family of distance functions. Unlike other approaches that aim to select the best distance function for a particular context, we use several distances and combine them in a convenient way. To this end, we adopt a classical similarity learning approach and face the problem as a standard supervised machine lea…
Effort in Semi-Automatized Subtitling Processes
2020
The presented study investigates the impact of automatic speech recognition (ASR) and assisting scripts on effort during transcription and translation processes, two main subprocesses of interlingual subtitling. Applying keylogging and eye tracking, this study takes a first look at how the integration of ASR impacts these subprocesses. 12 professional subtitlers and 13 translation students were recorded performing two intralingual transcriptions and three translation tasks to evaluate the impact on temporal, technical, and cognitive effort, and split-attention. Measures include editing time, visit count and duration, insertions, and deletions. The main findings show that, in both tasks, ASR…
Honeybees can recognise images of complex natural scenes for use as potential landmarks
2008
SUMMARY The ability to navigate long distances to find rewarding flowers and return home is a key factor in the survival of honeybees (Apis mellifera). To reliably perform this task, bees combine both odometric and landmark cues,which potentially creates a dilemma since environments rich in odometric cues might be poor in salient landmark cues, and vice versa. In the present study, honeybees were provided with differential conditioning to images of complex natural scenes, in order to determine if they could reliably learn to discriminate between very similar scenes, and to recognise a learnt scene from a novel distractor scene. Choices made by individual bees were modelled with signal detec…
Texture analysis with statistical methods for wheat ear extraction
2007
In agronomic domain, the simplification of crop counting, necessary for yield prediction and agronomic studies, is an important project for technical institutes such as Arvalis. Although the main objective of our global project is to conceive a mobile robot for natural image acquisition directly in a field, Arvalis has proposed us first to detect by image processing the number of wheat ears in images before to count them, which will allow to obtain the first component of the yield. In this paper we compare different texture image segmentation techniques based on feature extraction by first and higher order statistical methods which have been applied on our images. The extracted features are…
The coupling between face and emotion recognition from early adolescence to young adulthood
2020
Abstract In the present study, we investigated whether differentiation occurs between identity and emotion processing as development in both domains proceeds across adolescence and during the transition into young adulthood. A sample of 343 participants between 11 and 24 years performed the Glasgow Face Matching Task ( Burton, White, & McNeill, 2010 ) for identity-based face recognition and the Cambridge Face-Battery ( Golan, Baron-Cohen, & Hill, 2006 ) for complex emotion recognition. Our results show adult levels of face recognition by the end of early adolescence, while complex emotion recognition continues to develop into young adulthood. Although each ability matures at different rate,…