Binary Image Classification: A Genetic Programming Approach to the Problem of Limited Training Instances

Zhang

IEEE Trans. Evol. Computat.

et al. 2017

Self Cite

The goodness of the features extracted from the instances and the number of training instances are two key components in machine learning, and building an effective model is largely affected by these two factors. Acquiring a large number of training instances is very expensive in some situations such as in the medical domain. Designing a good feature set, on the other hand, is very hard and often requires domain expertise. In computer vision, image descriptors have emerged to automate feature detection and extraction; however, domain-expert intervention is typically needed to develop these descriptors. The aim of this paper is to utilise Genetic Programming to automatically construct a rotation-invariant image descriptor by synthesising a set of formulae using simple arithmetic operators and first-order statistics, and determining the length of the feature vector simultaneously using only two instances per class. Using seven texture classification image datasets, the performance of the proposed method is evaluated and compared against eight domain-expert hand-crafted image descriptors. Quantitatively, the proposed method has significantly outperformed, or achieved comparable performance to, the competitor methods. Qualitatively, the analysis shows that the descriptors evolved by the proposed method can be interpreted.

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confidence: 61%

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confidence: 86%

Keypoints Detection and Feature Extraction: A Dynamic Genetic Programming Approach for Evolving Rotation-Invariant Texture Image Descriptors

Zhang

IEEE Trans. Evol. Computat.

et al. 2017

Self Cite

“…GP has been widely used for image analysis [35], [36], [37], [38], where typical works are reviewed in this section. A detailed description of this work is beyond the scope of this article, readers are refereed to [39], [40], [41], [42], [43], [44], [45] for more work on EC or GP for image analysis.…”

Section: Image Analysis Using Genetic Programmingmentioning

confidence: 99%

Cross-Domain Reuse of Extracted Knowledge in Genetic Programming for Image Classification

Iqbal

Xue

IEEE Trans. Evol. Computat.

et al. 2017

Self Cite

Genetic programming (GP) is a well-known evolutionary computation technique, which has been successfully used to solve various problems, such as optimisation, image analysis and classification. Transfer learning is a type of machine learning approach that can be used to solve complex tasks. Transfer learning has been introduced to genetic programming to solve complex Boolean and symbolic regression problems with some promise. However, the use of transfer learning with genetic programming has not been investigated to address complex image classification tasks with noise and rotations, where GP cannot achieve satisfactory performance, but GP with transfer learning may improve the performance. In this paper, we propose a novel approach based on transfer learning and genetic programming to solve complex image classification problems by extracting and reusing blocks of knowledge/information, which are automatically discovered from similar as well as different image classification tasks during the evolutionary process. The proposed approach is evaluated on three texture data sets and three office data sets of image classification benchmarks, and achieves better classification performance than the state-of-the-art image classification algorithm. Further analysis on the evolved solutions/trees shows that the proposed approach with transfer learning can successfully discover and reuse knowledge/information extracted from similar or different problems to improve its performance on complex image classification problems.

“…In [4], two GP-based methods are studied for binary image classification tasks. These methods aim at evolving a classifier using a few instances per class.…”

Section: Related Workmentioning

confidence: 99%

“…Feature construction is the process of creating new, high-level features, often by combining multiple existing features [14,4]. Constructed features generally better describe an instance than a single existing feature, reducing the number of features required, which reduces the size of the search space a classifier must train on.…”

Section: Introductionmentioning

confidence: 99%

Genetic Programming for Region Detection, Feature Extraction, Feature Construction and Classification in Image Data

Lensen

Lecture Notes in Computer Science

Zhang

et al. 2016

Self Cite

Image analysis is a key area in the computer vision domain that has many applications. Genetic Programming (GP) has been successfully applied to this area extensively, with promising results. Highlevel features extracted from methods such as Speeded Up Robust Features (SURF) and Histogram of Oriented Gradients (HoG) are commonly used for object detection with machine learning techniques. However, GP techniques are not often used with these methods, despite being applied extensively to image analysis problems. Combining the training process of GP with the powerful features extracted by SURF or HoG has the potential to improve the performance by generating high-level, domaintailored features. This paper proposes a new GP method that automatically detects different regions of an image, extracts HoG features from those regions, and simultaneously evolves a classifier for image classification. By extending an existing GP region selection approach to incorporate the HoG algorithm, we present a novel way of using high-level features with GP for image classification. The ability of GP to explore a large search space in an efficient manner allows all stages of the new method to be optimised simultaneously, unlike in existing approaches. The new approach is applied across a range of datasets, with promising results when compared to a variety of well-known machine learning techniques. Some high-performing GP individuals are analysed to give insight into how GP can effectively be used with high-level features for image classification.