Simple Gabor feature space for invariant object recognition

Kyrki, Ville; Kämäräinen, Joni-Kristian; Kälviäinen, Heikki

doi:10.1016/j.patrec.2003.10.008

Cited by 194 publications

(125 citation statements)

References 14 publications

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“…In the literature there are studies of time-frequency tools such as a Gabor filter or a wavelet transform which are used for extracting features from the texture of an image (Grigorescu et al, 2002;Huang et al, 2003;Kyrki et al, 2004;Li and Shawe-Taylor, 2005;Tai, 2007). The classifier is trained based on some labelled texture features as the training set, used to classify unlabelled texture features of images into some pre-defined classes.…”

Section: Selected Topics Of Classification Based On Time-frequency Rementioning

confidence: 99%

Classification in the Gabor time-frequency domain of non-stationary signals embedded in heavy noise with unknown statistical distribution

Swiercz¹

2010

International Journal of Applied Mathematics and Computer Science

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A new supervised classification algorithm of a heavily distorted pattern (shape) obtained from noisy observations of nonstationary signals is proposed in the paper. Based on the Gabor transform of 1-D non-stationary signals, 2-D shapes of signals are formulated and the classification formula is developed using the pattern matching idea, which is the simplest case of a pattern recognition task. In the pattern matching problem, where a set of known patterns creates predefined classes, classification relies on assigning the examined pattern to one of the classes. Classical formulation of a Bayes decision rule requires a priori knowledge about statistical features characterising each class, which are rarely known in practice. In the proposed algorithm, the necessity of the statistical approach is avoided, especially since the probability distribution of noise is unknown. In the algorithm, the concept of discriminant functions, represented by Frobenius inner products, is used. The classification rule relies on the choice of the class corresponding to the max discriminant function. Computer simulation results are given to demonstrate the effectiveness of the new classification algorithm. It is shown that the proposed approach is able to correctly classify signals which are embedded in noise with a very low SNR ratio. One of the goals here is to develop a pattern recognition algorithm as the best possible way to automatically make decisions. All simulations have been performed in Matlab. The proposed algorithm can be applied to non-stationary frequency modulated signal classification and non-stationary signal recognition.

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Section: Selected Topics Of Classification Based On Time-frequency Rementioning

confidence: 99%

Classification in the Gabor time-frequency domain of non-stationary signals embedded in heavy noise with unknown statistical distribution

Swiercz¹

2010

International Journal of Applied Mathematics and Computer Science

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“…Most successful idea in this area employ time consuming algorithms using wavelet, Gabor filter or Hough transform [1,2,3], which are not applicable in real-time applications such as environment monitoring.…”

Section: Previous Workmentioning

confidence: 99%

Object Modeling for Multicamera Correspondence Using Fuzzy Region Color Adjacency Graphs

Khalili

Kasaei

2008

Communications in Computer and Information Science

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In this paper, a novel moving object modeling suitable for multicamera correspondence is introduced. Taking into consideration the color and motion features of foreground objects in each independent video stream, our method segments the existing moving objects and constructs a graph-based structure to maintain the relational information of each segment. Using such graph structures reduces our correspondence problem to a subgraph optimal isomorphism problem. The proposed method is robust against various resolutions and orientations of objects at each view. Our system uses the fuzzy logic to employ a human-like color perception in its decision making stage in order to handle color inconstancy which is a common problem in multiview systems. The computational cost of the proposed method is made low to be applied in real-time applications. Also, it can solve the partial occlusion problem more precisely than the Meanshif occlusion solver by 15.7%.

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“…In the spatial domain, the 2D Gabor filter is a Gaussian kernel function modulated by a sinusoidal plane wave (Daugman, 1988;Kyrki et al, 2004) …”

Section: D Gabor Filtersmentioning

confidence: 99%

MutualBoost learning for selecting Gabor features for face recognition

Shen

2006

Pattern Recognition Letters

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This paper describes an improved boosting algorithm, the MutualBoost algorithm, and its application in developing a fast and robust Gabor feature based face recognition system. The algorithm uses mutual information to eliminate redundancy among Gabor features selected using the AdaBoost algorithm. Selected Gabor features are then subjected to Generalized Discriminant Analysis (GDA) for class separability enhancement before being used for face recognition. Compared with one of the top performers in the 2004 face verification competition, our method demonstrates clear advantages in classification accuracy, memory and computation. The method has been tested on the whole FERET database using the FERET evaluation protocol. Significant improvement in performance is observed. For example, existing Gabor based methods use a huge number of Gabor features, our method needs only hundreds of Gabor features to achieve very high classification accuracy. Due to substantially reduced feature dimension, memory and computation costs are reduced significantly -only 4 s are needed to recognize 200 face images.

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Simple Gabor feature space for invariant object recognition

Cited by 194 publications

References 14 publications

Classification in the Gabor time-frequency domain of non-stationary signals embedded in heavy noise with unknown statistical distribution

Classification in the Gabor time-frequency domain of non-stationary signals embedded in heavy noise with unknown statistical distribution

Object Modeling for Multicamera Correspondence Using Fuzzy Region Color Adjacency Graphs

MutualBoost learning for selecting Gabor features for face recognition

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