1,229 research outputs found
Selecting features for object detection using an AdaBoost-compatible evaluation function
This paper addresses the problem of selecting features in a visual object detection setup where a detection algorithm is applied to an input image represented by a set of features. The set of features to be employed in the test stage is prepared in two training-stage steps. In the first step, a feature extraction algorithm produces a (possibly large) initial set of features. In the second step, on which this paper focuses, the initial set is reduced using a selection procedure. The proposed selection procedure is based on a novel evaluation function that measures the utility of individual features for a certain detection task. Owing to its design, the evaluation function can be seamlessly embedded into an AdaBoost selection framework. The developed selection procedure is integrated with state-of-the-art feature extraction and object detection methods. The presented system was tested on five challenging detection setups. In three of them, a fairly high detection accuracy was effected by as few as six features selected out of several hundred initial candidates
Adaptive visual sampling
PhDVarious visual tasks may be analysed in the context of sampling from the visual field. In visual
psychophysics, human visual sampling strategies have often been shown at a high-level to
be driven by various information and resource related factors such as the limited capacity of
the human cognitive system, the quality of information gathered, its relevance in context and
the associated efficiency of recovering it. At a lower-level, we interpret many computer vision
tasks to be rooted in similar notions of contextually-relevant, dynamic sampling strategies
which are geared towards the filtering of pixel samples to perform reliable object association. In
the context of object tracking, the reliability of such endeavours is fundamentally rooted in the
continuing relevance of object models used for such filtering, a requirement complicated by realworld
conditions such as dynamic lighting that inconveniently and frequently cause their rapid
obsolescence. In the context of recognition, performance can be hindered by the lack of learned
context-dependent strategies that satisfactorily filter out samples that are irrelevant or blunt the
potency of models used for discrimination. In this thesis we interpret the problems of visual
tracking and recognition in terms of dynamic spatial and featural sampling strategies and, in this
vein, present three frameworks that build on previous methods to provide a more flexible and
effective approach.
Firstly, we propose an adaptive spatial sampling strategy framework to maintain statistical object
models for real-time robust tracking under changing lighting conditions. We employ colour
features in experiments to demonstrate its effectiveness. The framework consists of five parts:
(a) Gaussian mixture models for semi-parametric modelling of the colour distributions of multicolour
objects; (b) a constructive algorithm that uses cross-validation for automatically determining
the number of components for a Gaussian mixture given a sample set of object colours; (c) a
sampling strategy for performing fast tracking using colour models; (d) a Bayesian formulation
enabling models of object and the environment to be employed together in filtering samples by
discrimination; and (e) a selectively-adaptive mechanism to enable colour models to cope with
changing conditions and permit more robust tracking.
Secondly, we extend the concept to an adaptive spatial and featural sampling strategy to deal
with very difficult conditions such as small target objects in cluttered environments undergoing
severe lighting fluctuations and extreme occlusions. This builds on previous work on dynamic
feature selection during tracking by reducing redundancy in features selected at each stage as
well as more naturally balancing short-term and long-term evidence, the latter to facilitate model
rigidity under sharp, temporary changes such as occlusion whilst permitting model flexibility
under slower, long-term changes such as varying lighting conditions. This framework consists of
two parts: (a) Attribute-based Feature Ranking (AFR) which combines two attribute measures;
discriminability and independence to other features; and (b) Multiple Selectively-adaptive Feature
Models (MSFM) which involves maintaining a dynamic feature reference of target object
appearance. We call this framework Adaptive Multi-feature Association (AMA). Finally, we present an adaptive spatial and featural sampling strategy that extends established
Local Binary Pattern (LBP) methods and overcomes many severe limitations of the traditional
approach such as limited spatial support, restricted sample sets and ad hoc joint and disjoint statistical
distributions that may fail to capture important structure. Our framework enables more
compact, descriptive LBP type models to be constructed which may be employed in conjunction
with many existing LBP techniques to improve their performance without modification. The
framework consists of two parts: (a) a new LBP-type model known as Multiscale Selected Local
Binary Features (MSLBF); and (b) a novel binary feature selection algorithm called Binary Histogram
Intersection Minimisation (BHIM) which is shown to be more powerful than established
methods used for binary feature selection such as Conditional Mutual Information Maximisation
(CMIM) and AdaBoost
Modelization and validation of acute toxicity on aquatic fauna applying classification and regression methods
L’objectiu principal d’aquest projecte es el d’aplicar diferents mètodes de classificació i de regressió
per a poder predir, en un futur, la toxicitat d’una substància química sobre una espècie de peix,
Pimephales promelas, a partir de la seva estructura química. La finalitat és poder evitar la mort i el
patiment d’aquests animals utilitzats per a les avaluacions toxicològiques Per això, s’ha obtingut una
base de dades de 908 molècules amb la seva toxicitat sobre el peix d’estudi i, a través de l’empresa
KODE Chemoinformatics s’han obtingut aproximadament 4000 indicadors moleculars per a cada
substància els quals s’han reduït a 2500 a través d’un filtratge. Un cop obtinguda la base de dades i
amb ajuda del programa Python, s’ha obtingut diferents combinacions de variables que s’han testat
amb mètodes de Machine Learning de regressió y classificació aconseguint un error mitjà absolut
normalitzat d’un 7,3% com a mínim a partir dels mètodes de regressió aplicats i amb els de classificació
una precisió del 60%. Tot i l’obtenció d’uns resultats acceptables per regressió, no són prou fiables per
a substituir el mètode tradicional d’avaluació de la toxicitat d’un químic ja que un error de càlcul podria
esdevenir en resultats fatals
A Comparative Study of Ensemble Classifiers for Paddy Blast Disease Prediction Model
Paddy blast has become most epidemic disease in many rice growing countries. Various statistical methods have been used for the prediction of paddy blast but previously used methods failed in predicting diseases with good accuracy. However the need to develop new model that considers both weather factors and non weather data called blast disease data that influences paddy disease to grow. Given this point we developed ensemble classifier based paddy disease prediction model taking weather data from January 2013 to December 2019 from Agricultural and Horticulture Research Station Kathalgere Davangere District. For the predictive model we collected 7 kinds of weather data and 7 kinds of disease related data that includes Minimum Temperature, Maximum Temperature, Temperautre Difference,Relative Humidity, Stages of Paddy Cultivation, Varities of seeds, Season of cropping and so on. It is observed and analyzed that Minimum Temperature, Humidity and Rainfall has huge correlation with occurrence of disease. Since some of the variables are non numeric to convert them to numeric data one hot encoding approach is followed and to improve efficiency of ensemble classifiers 4 different filter based features selection methods are used such as Pearson’s correlation, Mutual information, ANNOVA F Value, Chi Square. Three different ensemble classifiers are used as predictive models and classifiers are compared it is observed that Bagging ensemble technique has achieved accuracy of 98% compared to Adaboost of 97% and Voting classifier of 88%. Other classification metrics are used evaluate different classifiers like precision, recall, F1 Score, ROC and precision recall score. Our proposed ensemble classifers for paddy blast disease prediction has achieved high precision and high recall but when the solutions of model are closely looked bagging classifier is better compared to other ensemble classifers that are proposed in predicting paddy blast disease
- …