Texture Image Retrieval based on Log-Gabor Features (original) (raw)

A modified Gabor function for content based image retrieval

Pattern Recognition Letters, 2007

As the Gabor filters are direction dependent, the Gabor transform of an image is to be performed for all chosen directions. Thus the set of angles used in Gabor feature extraction does affect the results in applications such as Content Based Image Retrieval (CBIR). In the present work, we modify the Gabor filter suitably in such a way that the modified function besides being free from the choice of angles is as effective as the Gabor function itself. Additionally, our method of extraction of features is invariant to rotation in images. Our simulation results demonstrate that the modified Gabor based method being useful for CBIR shows better retrieval performance than the standard Gabor based method.

Image Retrieval using GA Optimized Gabor Filter

Indian Journal of Science and Technology, 2016

Objective: A Hybrid content based image retrieval method is proposed in this paper. This method extracts color, tuned texture and shape features of the images in three successive phases. Methodology: In proposed system, color features are extracted using color histogram method in the first phase. The tuned texture features are extracted by employing GA optimized Gabor filters in second phase. Finally, shape features are extracted using the polygonal fitting algorithm. The best match output images of each phase are given as input images to the next phase to obtain 'S' best match images out of 'N' database images. Findings: The novelty of proposed system is that it employs a tunable filter that is tuned with the query image dynamically. The tuning of Gabor filter is implemented using GA in second phase. The proposed method shows improved retrieval rate in terms of average recall and average precision compared to the existing systems. The computation complexity is also found to be less than other existing methods. Applications: It can be employed in numerous fields such as medical, satellite, multimedia, and surveillance imaging systems, etc. where the retrieval of related images from huge databases is critical task for analysis.

Gabor kernels for textured image representation and classificacion

A Gabor based representation for textured images is proposed. Instead of the ordinary filter bank, a reproducing kernel representation is constructed consisting of a sum of several local reproducing kernels. The image representation coefficients are computed by a basis pursuit procedure, and are then considered as the feature vectors. The feature vectors are used to construct a kernel for a support vector classifier. Results are presented for a set of oriented texture images.

Comparison of texture features based on Gabor filters

IEEE Transactions on Image Processing, 2002

The performance of a number of texture feature operators is evaluated. The features are all based on the local spectrum which is obtained by a bank of Gabor filters. The comparison is made using a quantitative method which is based on Fisher's criterion. It is shown that, in general, the discrimination effectiveness of the features increases with the amount of post-Gabor processing.

A Fast Gabor Filter Approach for Multi-Channel Texture Feature Discrimination

Texture is a very important concept for many image understanding and pattern classification applications. The analysis of texture can be performed by the multi-channel filtering theory, a classical theory for texture perception based on the early stages of human visual system. This approach decomposes an image into a set of responses given by a bank of Gabor filters, that nearly covers in an uniformly manner the spatial-frequency domain. This approach relies on the image dimensions, and the number of kernels in a bank of Gabor filters varies according to the number of combinations between frequencies and orientations. In many practical applications, this large number of combinations makes quickly unfeasible the computation of the whole bank of filters. To ease this problem, in this paper we propose a multi-channel filtering where the Gabor bank for texture discrimination is computed in parallel in a graphics processing unit (GPU). Experimental results show an improvement of 8.78 times for feature extraction when compared against the corresponding CPU-based approach.

Fusion of Gabor filter and co-occurrence probability features for texture recognition

IEEE Transactions on Image Processing, 2005

This paper explores a design-based method to fuse Gabor filter features and co-occurrence probability features for improved texture recognition. The fused feature set utilizes both the Gabor filter's capability of accurately capturing lower frequency texture information and the co-occurrence probability's capability in texture information relevant to higher frequency components. Fisher linear discriminant analysis indicates that the fused features have much higher feature space separation than the pure features. Image texture ...

Texture classification using Gabor wavelets based rotation invariant features

Pattern Recognition Letters, 2006

Texture based image analysis techniques have been widely employed in the interpretation of earth cover images obtained using remote sensing techniques, seismic trace images, medical images and in query by content in large image data bases. The development in multiresolution analysis such as wavelet transform leads to the development of adequate tools to characterize different scales of textures effectively. But, the wavelet transform lacks in its ability to decompose input image into multiple orientations and this limits their application to rotation invariant image analysis. This paper presents a new approach for rotation invariant texture classification using Gabor wavelets. Gabor wavelets are the mathematical model of visual cortical cells of mammalian brain and using this, an image can be decomposed into multiple scales and multiple orientations. The Gabor function has been recognized as a very useful tool in texture analysis, due to its optimal localization properties in both spatial and frequency domain and found widespread use in computer vision. Texture features are found by calculating the mean and variance of the Gabor filtered image. Rotation normalization is achieved by the circular shift of the feature elements, so that all images have the same dominant direction. The texture similarity measurement of the query image and the target image in the database is computed by minimum distance criterion.

Image retrieval with Gabor-Wavelet-Networks

2002

Image retrieval with dynamically extracted features compares user-defined regions of interest with all sections of the pictures stored in an image database. Thereby, objects regardless of the actual pictorial environment are of interest. This paper discusses an application of Gabor-Wavelet-Networks as a solution of the object-based search. In contrast to existing template matching methods deviations regarding scaling and rotation can be partly compensated. However, the improved retrieval quality and flexibility requires large computational resources, which are satisfied by the developed cluster-based architecture. The retrieval quality and the obtained speedup are examined by a number of experiments.

SASI: a generic texture descriptor for image retrieval

Pattern Recognition, 2003

In this paper, a generic texture descriptor, namely, Statistical Analysis of Structural Information (SASI) is introduced as a representation of texture. SASI is based on statistics of clique autocorrelation coefficients, calculated over structuring windows. SASI defines a set of clique windows to extract and measure various structural properties of texture by using a spatial multi-resolution method. Experimental results, performed on various image databases, indicate that SASI is more successful then the Gabor Filter descriptors in capturing small granularities and discontinuities such as sharp corners and abrupt changes. Due to the flexibility in designing the clique windows, SASI reaches higher average retrieval rates compared to Gabor Filter descriptors. However, the price of this performance is increased computational complexity. complex due to changes in orientation, scale or other visual appearance such as brightness and contrast .