Texture classification with thousands of features

2012 IEEE Congress on Evolutionary Computation

et al. 2012

Abstract-Trace transform is one representation of images that uses different functionals applied on the image function. When the functional is integral, it becomes identical to the well-known Radon transform, which is a useful tool in computed tomography medical imaging. The key question in Trace transform is to select the best combination of the Trace functionals to produce the optimal triple feature, which is a challenging task. In this paper, we adopt a multi-objective evolutionary algorithm adapted from the elitist nondominated sorting genetic algorithm (NSGA-II), an evolutionary algorithm that has shown to be very efficient for multi-objective optimization, to select the best functionals as well as the optimal number of projections used in Trace transform to achieve invariant image identification. This is achieved by minimizing the within-class variance and maximizing the between-class variance. To enhance the computational efficiency, the Trace parameters are calculated offline and stored, which are then used to calculate the triple features in the evolutionary optimization. The proposed Evolutionary Trace Transform (ETT) is empirically evaluated on various images from fish database. It is shown that the proposed algorithm is very promising in that it is computationally efficient and considerably outperforms existing methods in literatures.

Section: The Trace Transformmentioning

confidence: 99%

Evolutionary multi-objective optimization of trace transform for invariant feature extraction

2012 IEEE Congress on Evolutionary Computation

et al. 2012

“…Trace transform can be seen as a more general case of Radon transform, which has been successfully applied to many image processing tasks such as image database retrieval [10], texture classification [11], insect footprint recognition [12] and character recognition [13]. Trace transform involves calculating functionals along straight lines crossing the image in different directions.…”

Section: Introductionmentioning

confidence: 99%

“…Trace transform has received much attention in the field of image analysis and invariant feature extraction due to its effectiveness in producing multiple features that describe an image [10], [11]. Kadyrov and Petrou [10], [14]- [18] proposed the Trace transform and a theory for invariant Triple feature extraction.…”

Section: Introductionmentioning

confidence: 99%

Evolutionary Multiobjective Image Feature Extraction in the Presence of Noise

2015

IEEE Trans. Cybern.

Abstract-A Pareto-based evolutionary multi-objective approach is adopted to optimize the functionals in the Trace Transform for extracting image features that are robust to noise and invariant to geometric deformations such as rotation, scale and translation (RST). To this end, sample images with noise and with RST distortion are employed in the evolutionary optimization of the Trace Transform, which is termed evolutionary Trace Transform with noise (ETTN). Experimental studies on a fish image database and the Columbia COIL-20 image database show that the ETTN optimized on a few low-resolution images from the fish database can extract robust and RST invariant features from the standard images in the fish database as well as in the COIL-20 database. These results demonstrate that the proposed ETTN is very promising in that it is computationally efficient, invariant to RST deformation, robust to noise and generalizable.

“…In fact, thousand of features can be constructed using different Trace transform functionals by using different combinations of these functionals. Therefore, one may consider Trace transform as a mathematical tool to represent brain sub-conscious which we can not usually identify [4].…”

Section: Introductionmentioning

confidence: 99%

“…Trace Transform has been successfully applied to many image processing tasks such as image database retrieval [2], texture classification [4] and characters recognition [5]. Evolutionary Trace Transform (ETT) [6] has recently been developed to construct efficient Trace transform triple features to represent an image.…”

Section: Introductionmentioning

confidence: 99%

A Comparative Study of Multi-objective Evolutionary Trace Transform Methods for Robust Feature Extraction

Lecture Notes in Computer Science

et al. 2013

Abstract. Recently, Evolutionary Trace Transform (ETT) has been developed to extract efficient features (called triple features) for invariant image identification using multi-objective evolutionary algorithms. This paper compares two methods of Evolutionary Trace Transform (method I and II) evolved through similar objectives by minimizing the withinclass variance (Sw) and maximizing the between-class variance (S b ) of image features. However, each solution on the Pareto front of method I represents one triple features (i.e. 1D) to be combined with another solution to construct 2D feature space, whereas each solution on the Pareto front of method II represents a complete pair of triple features (i.e. 2D). Experimental results show that both methods are able to produce stable and consistent features. Moreover, method II has denser solutions distributed in the convex region of the Pareto front than in method I. Nevertheless, method II takes longer time to evolve than method I. Although the Trace transforms are evolved offline on one set of low resolution (64 × 64) images, they can be applied to extract features from various standard 256 × 256 images.