Image Categorization Using ESFS: A New Embedded Feature Selection Method Based on SFS

Fu, Huanzhang; Xiao, Zhongzhe; Dellandréa, Emmanuel; Dou, Wei; Chen, Liming

doi:10.1007/978-3-642-04697-1_27

Cited by 18 publications

(6 citation statements)

References 18 publications

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“…The embedded method [6] integrates the feature generation strategy and the classification of the quality of the selected subset into the learning algorithm, thus the link established between feature selection and classification is stronger than in the wrapper method [22]. This leads to a combination of the advantages of filter and wrapper based methods with a reduced computational time [9,22]. The implicit feature generation built into learning algorithms such as decision tree induction algorithms makes CART, ID3 and C4.5 embedded learning methods [41].…”

Section: Embedded Methodsmentioning

confidence: 99%

Feature selection and classification using age layered population structure genetic programming

Awuley

Ross

2016

2016 IEEE Congress on Evolutionary Computation (CEC)

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The curse of dimensionality is a major problem in the fields of machine learning, data mining and knowledge discovery. Exhaustive search for the most optimal subset of relevant features from a high dimensional dataset is NP hard. Sub-optimal population based stochastic algorithms such as GP and GA are good choices for searching through large search spaces, and are usually more feasible than exhaustive and deterministic search algorithms. On the other hand, population based stochastic algorithms often suffer from pre-mature convergence on mediocre sub-optimal solutions. The Age Layered Population Structure (ALPS) is a novel meta-heuristic for overcoming 1. Brian J. Ross for accepting me as a student and providing two years of excellent supervision, guidance, and funding.I could not have gotten a better supervisor.2. Beatrice Ombuki-Berman and Sheridan Houghten for their participation on the supervisory committee.3. Cale Fairchild for providing an outstanding technical support and knowledge on computing clusters and Linux expertise.4. The Computer Science department and Brock Graduate School for providing me with the privilege, facilities, funding and enabling environment to accomplish something great. Love is patient and kind. Love is not jealous. It does not brag, does not get puffed up, does not behave indecently, does not look for its own interests, does not become provoked. It does not keep account of the injury. It does not rejoice over unrighteousness, but rejoices with the truth. It bears all things, belives all things, hopes all things, endures all things. Love never fails.

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Section: Embedded Methodsmentioning

confidence: 99%

Feature selection and classification using age layered population structure genetic programming

Awuley

Ross

2016

2016 IEEE Congress on Evolutionary Computation (CEC)

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“…Used as a filter method, we carried out experiments aiming at comparing the behavior of our ESFS with other filter featureselection techniques, including Fischer filter method, PCA, and SFS. Using Berlin dataset for emotional speech recognition and Simplicity dataset for visual object recognition, our ESFS displayed better performance, showing its effectiveness in the selection of relevant features [35].…”

Section: T-norm a T-norm Is A Functionmentioning

confidence: 97%

Classification of Emotional Speech Based on an Automatically Elaborated Hierarchical Classifier

Xiao

Dellandréa

Dou

et al. 2011

ISRN Signal Processing

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Current machine-based techniques for vocal emotion recognition only consider a finite number of clearly labeled emotional classes whereas the kinds of emotional classes and their number are typically application dependent. Previous studies have shown that multistage classification scheme, because of ambiguous nature of affect classes, helps to improve emotion classification accuracy. However, these multistage classification schemes were manually elaborated by taking into account the underlying emotional classes to be discriminated. In this paper, we propose an automatically elaborated hierarchical classification scheme (ACS), which is driven by an evidence theory-based embedded feature-selection scheme (ESFS), for the purpose of application-dependent emotions' recognition. Experimented on the Berlin dataset with 68 features and six emotion states, this automatically elaborated hierarchical classifier (ACS) showed its effectiveness, displaying a 71.38% classification accuracy rate compared to a 71.52% classification rate achieved by our previously dimensional model-driven but still manually elaborated multistage classifier (DEC). Using the DES dataset with five emotion states, our ACS achieved a 76.74% recognition rate compared to a 81.22% accuracy rate displayed by a manually elaborated multistage classification scheme (DEC).

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“…They have the advantages of wrapper methods that the selection of feature subsets has an interaction with the learning tasks, so the selected features tend to be more effective than those generated by filter methods. Furthermore, they are less computationally complex than wrappers, as the feature selection is directly included in the construction of a learning model during the training process [60]. However, they are more complex conceptually, and modifications to the learning algorithm may cause poor performance [113].…”

Section: Embedded Approachesmentioning

confidence: 99%

Genetic Programming for Supervised Figure-ground Image Segmentation

Liang¹

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<p>Figure-ground segmentation is a process of separating regions of interest from unimportant backgrounds. It is essential to various applications in computer vi- sion and image processing, e.g. object tracking and image editing, as they are only interested in certain regions of an image and use figure-ground segmenta- tion as a pre-processing step. Traditional figure-ground segmentation methods often require heavy human workload (e.g. ground truth labeling), and/or rely heavily on human guidance (e.g. locating an initial model), accordingly cannot easily adapt to diverse image domains. Evolutionary computation (EC) is a family of algorithms for global optimi- sation, which are inspired by biological evolution. As an EC technique, genetic programming (GP) can evolve algorithms automatically for complex problems without pre-defining solution models. Compared with other EC techniques, GP is more flexible as it can utilise complex and variable-length representations (e.g. trees) of candidate solutions. It is hypothesised that this flexibility of GP makes it possible to evolve better solutions than those designed by experts. However, there have been limited attempts at applying GP to figure-ground segmentation. In this thesis, GP is enabled to successfully address figure-ground segmentation through evolving well-performing segmentors and generating effective features. The objectives are to investigate various image features as inputs of GP, develop multi-objective approaches, develop feature selection/construction methods, and conduct further evaluations of the proposed GP methods. The following new methods have been developed. Effective terminal sets of GP are investigated for figure-ground segmentation, covering three general types of image features, i.e. colour/brightness, texture and shape features. Results show that texture features are more effective than intensities and shape features as they are discriminative for different materials that foreground and background regions normally belong to (e.g. metal or wood). Two new multi-objective GP methods are proposed to evolve figure-ground segmentors, aiming at producing solutions balanced between the segmentation performance and solution complexity. Compared with a reference method that does not consider complexity and a parsimony pressure based method (a popular bloat control technique), the proposed methods can significantly reduce the solution size while achieving similar segmentation performance based on the Mann- Whitney U-Test at the significance level 5%. GP is introduced for the first time to conduct feature selection for figure- ground segmentation tasks, aiming to maximise the segmentation performance and minimise the number of selected features. The proposed methods produce feature subsets that lead to solutions achieving better segmentation performance with lower features than those of two benchmark methods (i.e. sequential forward selection and sequential backward selection) and the original full feature set. This is due to GP’s high search ability and higher likelihood of finding the global optima. GP is introduced for the first time to construct high-level features from primitive image features, which aims to improve the image segmentation performance, especially on complex images. By considering linear/non-linear interactions of the original features, the proposed methods construct fewer features that achieve better segmentation performance than the original full feature set. This investigation has shown that GP is suited for figure-ground image segmentation for the following reasons. Firstly, the proposed methods can evolve segmentors with useful class characteristic patterns to segment various types of objects. Secondly, the segmentors evolved from one type of foreground object can generalise well on similar objects. Thirdly, both the selected and constructed features of the proposed GP methods are more effective than original features, with the selected/constructed features being better for subsequent tasks. Finally, compared with other segmentation techniques, the major strengths of GP are that it does not require pre-defined problem models, and can be easily adapted to diverse image domains without major parameter tuning or human intervention.</p>

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Image Categorization Using ESFS: A New Embedded Feature Selection Method Based on SFS

Cited by 18 publications

References 18 publications

Feature selection and classification using age layered population structure genetic programming

Feature selection and classification using age layered population structure genetic programming

Classification of Emotional Speech Based on an Automatically Elaborated Hierarchical Classifier

Genetic Programming for Supervised Figure-ground Image Segmentation

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