Jin-Seon Lee

Hybrid genetic algorithms for feature selection

This paper proposes a novel hybrid genetic algorithm for feature selection. Local search operations are devised and embedded in hybrid GAs to fine-tune the search. The operations are parameterized in terms of their fine-tuning power, and their effectiveness and timing requirements are analyzed and compared. The hybridization technique produces two desirable effects: a significant improvement in the final performance and the acquisition of subset-size control. The hybrid GAs showed better convergence properties compared to the classical GAs. A method of performing rigorous timing analysis was developed, in order to compare the timing requirement of the conventional and the proposed algorithms. Experiments performed with various standard data sets revealed that the proposed hybrid GA is superior to both a simple GA and sequential search algorithms.

Analysis of class separation and combination of class-dependent features for handwriting recognition

In this paper, we propose a new approach to combine multiple features in handwriting recognition based on two ideas: feature selection-based combination and class dependent features. A nonparametric method is used for feature evaluation, and the first part of this paper is devoted to the evaluation of features in terms of their class separation and recognition capabilities. In the second part, multiple feature vectors are combined to produce a new feature vector. Based on the fact that a feature has different discriminating powers for different classes, a new scheme of selecting and combining class-dependent features is proposed. In this scheme, a class is considered to have its own optimal feature vector for discriminating itself from the other classes. Using an architecture of modular neural networks as the classifier, a series of experiments were conducted on unconstrained handwritten numerals. The results indicate that the selected features are effective in separating pattern classes and the new feature vector derived from a combination of two types of such features further improves the recognition rate.

Local search-embedded genetic algorithms for feature selection

This paper proposes a novel hybrid genetic algorithm for the feature selection. Local search operations used to improve chromosomes are defined and embedded in hybrid GAs. The hybridization gives two desirable effects: improving the final performance significantly and acquiring control of subset size. For the implementation reproduction by readers, we provide detailed information of GA procedure and parameter setting. Experimental results reveal that the proposed hybrid GA is superior to a classical GA and sequential search algorithms.

Binary classification trees for multi-class classification problems

This paper proposes a binary classification tree aiming atsolving multi-class classification problems using binaryclassifiers. The tree design is achieved in a way that aclass group is partitioned into two distinct subgroups at anode. The node adopts the class-modular scheme toimprove the binary classification capability. Thepartitioning is formulated as an optimization problemand a genetic algorithm is proposed to solve theoptimization problem. The binary classification tree iscompared to the conventional methods in terms ofclassification accuracy and timing efficiency.Experiments were performed with numeral recognitionand touching-numeral pair recognition.

Using class separation for feature analysis and combination of class-dependent features

We analyze the class separation of the features in handwriting recognition. Behaviors of measurement tools are studied with a partial and full classifications. A new scheme of selecting and combining class-dependent features is proposed. In this scheme, a class is considered to have its own optimal feature vector for discriminating itself from the other classes. Using an architecture of modular neural networks as the classifier, a series of experiments have been conducted on totally unconstrained handwritten numerals. The results indicate that the selected features are effective in separating pattern classes and the new feature vector derived from a combination of two types of such features further improves the recognition rate.

Multi-scale Image Segmentation Using MSER

Recently several research works propose image segmentation algorithms using MSER. However they aim at segmenting out specific regions corresponding to user-defined objects. This paper proposes a novel algorithm based on MSER which segments natural images without user intervention and captures multi-scale structure. The algorithm collects MSERs and then partitions whole image plane by redrawing them in specific order. To denoise and smooth the region boundaries, hierarchical morphological operations are developed. To illustrate effectiveness of the algorithm’s multi-scale structure, effects of various types of LOD control are shown for image stylization.

A class-modularity for character recognition

A class-modular classifier can be characterized by two prominent features: low classifier complexity and independence of classes. While conventional character recognition systems adopting the class modularity are faithful to the first feature, they do not investigate the second one. Since a class can be handled independently of the other classes, the class-specific feature set and classifier architecture can be optimally designed for a specific class Here we propose a general framework for the class modularity that exploits fully both features and present four types of class-modular architecture. The neural network classifier is used for testing the framework A simultaneous selection of the feature set and network architecture is performed by the genetic algorithm. The effectiveness of the class-specific features and classifier architectures is confirmed by experimental results on the recognition of handwritten numerals.

Ant Colony Optimization for Feature Selection in Pattern Recognition

This paper propose a novel scheme called selective evaluation to improve convergence of ACO (ant colony optimization) for feature selection. The scheme cutdown the computational load by excluding the evaluation of unnecessary or less promising candidate solutions. The scheme is realizable in ACO due to the valuable information, pheromone trail which helps identify those solutions. With the aim of checking applicability of algorithms according to problem size, we analyze the timing requirements of three popular feature selection algorithms, greedy algorithm, genetic algorithm, and ant colony optimization. For a rigorous timing analysis, we adopt the concept of atomic operation. Experimental results showed that the ACO with selective evaluation was promising both in timing requirement and recognition performance.

Ant colony optimization with null heuristic factor for feature selection

Recently, the ant colony optimization (ACO) meta-heuristic has received more attention as an efficient searching method for feature selection. This paper addresses various solution representation schemes of ACO and their effectiveness with respect to whether they consider correlations between features. A generic code of ACO using on-edge representation is presented. The paper formulates the η-component by concentrating on the types of objects that participate in calculating the η value. Four schemes based on the formulation are compared in terms of the timing efficiency and accuracy. The experimental results showed that the null-η scheme is comparable to other schemes. We discuss the explanation of these conclusions.

Smooth Stroke Width Transform for Text Detection

The stroke width transform (SWT) is a generic operation for the task of detecting texts from natural images because the characters intrinsically have the elongated shape of nearly uniform width. The edge pairing technique was recently developed by Epshtein et al. and is popularly used due to its simplicity and effectiveness. However since the natural images are noisy and sensitive to variations, high degree of artifacts arises and it hinders subsequent processing of the text detection. This paper reformulates the SWT problem in a new way that searches for an optimal solution in 3-D space. We present an effective search algorithm called the aggregation approach, borrowed from the depth image reconstruction domain. The experiments showed that the algorithm produced a smooth SWT map which is better for subsequent processes.