Prabir Kumar Biswas

Texture image retrieval using new rotated complex wavelet filters

A new set of two-dimensional (2-D) rotated complex wavelet filters (RCWFs) are designed with complex wavelet filter coefficients, which gives texture information strongly oriented in six different directions (45/spl deg/ apart from complex wavelet transform). The 2-D RCWFs are nonseparable and oriented, which improves characterization of oriented textures. Most texture image retrieval systems are still incapable of providing retrieval result with high retrieval accuracy and less computational complexity. To address this problem, we propose a novel approach for texture image retrieval by using a set of dual-tree rotated complex wavelet filter (DT-RCWF) and dual-tree-complex wavelet transform (DT-CWT) jointly, which obtains texture features in 12 different directions. The information provided by DT-RCWF complements the information generated by DT-CWT. Features are obtained by computing the energy and standard deviation on each subband of the decomposed image. To check the retrieval performance, texture database D1 of 1856 textures from Brodatz album and database D2 of 640 texture images from VisTex image database is created. Experimental results indicates that the proposed method improves retrieval rate from 69.61% to 77.75% on database D1, and from 64.83% to 82.81% on database D2, in comparing with traditional discrete wavelet transform based approach. The proposed method also retains comparable levels of computational complexity.

Comparison of similarity metrics for texture image retrieval

Similarity metrics plays an important role in content-based image retrieval. The paper compares nine image similarity measures - Manhattan (L1), weighted-mean-variance (WMV), Euclidean (L2), Chebychev (L/spl infin/), Mahalanobis, Canberra, Bray-Curtis, squared chord and squared chi-squared distances - for texture image retrieval. A large texture database of 1856 images, derived from the Brodatz album, is used to check the retrieval performance. Features of all the database images were extracted using the Gabor wavelet. Experimental results on the Brodatz texture database indicate that the retrieval performance can be improved significantly by using the Canberra and Bray-Curtis distance metrics as compare to traditional Euclidean and Mahalanobis distance based approaches.

Texture image retrieval using rotated wavelet filters

A novel approach for texture image retrieval is proposed by using a new set of two-dimensional (2-D) rotated wavelet filters (RWF) and discrete wavelet transform (DWT) jointly. A new set of 2-D rotated wavelet improves characterization of diagonally oriented textures. Experimental results indicate that the proposed method improves retrieval rate from 70.09% to 78.44% on database D1, and from 75.62% to 80.78% on database D2, compared with the traditional DWT based approach. The proposed method also retains comparable levels of computational complexity.

A Survey on Current Content based Image Retrieval Methods

Retrieving information from the Web is becoming a common practice for internet users. However, the size and heterogeneity of the Web challenge the effectiveness of classical information retrieval techniques. Content-based retrieval of images and video has become a hot research area. The reason for this is the fact that we need effective and efficient techniques that meet user requirements, to access large volumes of digital images and video data. This paper gives a brief survey of current CBIR (Content Based Image Retrieval) methods and technical achievement in this area. The survey includes a large number of papers covering the research aspects of system design and applications of CBIR, image feature representation and extraction, Multidimensional indexing. Furthermore future research directions are suggested.

Rotation-Invariant Texture Image Retrieval Using Rotated Complex Wavelet Filters

This paper proposes a novel approach for rotation-invariant texture image retrieval by using set of dual-tree rotated complex wavelet filter (DT-RCWF) and DT complex wavelet transform (DT-CWT) jointly, which obtains texture features in 12 different directions. Two-dimensional RCWFs are nonseparable and oriented, which improves characterization of oriented textures. Robust and efficient isotropic rotationally invariant features are extracted from DT-RCWF and DT-CWT decomposed subbands. This paper demonstrates the effectiveness of this new set of features on four different sets of rotated and nonrotated databases. Experimental results indicate that the proposed method improves retrieval accuracy from 83.17% to 93.71% on a small size (208 images) nonrotated database D1, from 82.71% to 90.86% on a small size (208 images) rotated database D2, from 72.18% to 76.09% on a medium-size (640 images) rotated database D3, and from 64.17% to 78.93% on a large size (1856 images) rotated database D4, compared with the discrete wavelet transform-based approach. New method also retains complexity

Investigations on fuzzy thresholding based on fuzzy clustering

Thresholding, the problem of pixel classification is attempted here using fuzzy clustering algorithms. The segmented regions are fuzzy subsets, with soft partitions characterizing the region boundaries. The validity of the assumptions and thresholding schemes are investigated in the presence of distinct region proportions. The hard k means and fuzzy c means algorithms have been found useful when object and background regions are well balanced. Fuzzy thresholding is also formulated as extraction of normal densities to provide optimal partitions. Regional imbalances in gray distributions are taken care of in region normalized histograms.

Rotation and scale invariant texture features using discrete wavelet packet transform

Novel rotation and scale invariant features are proposed in this paper using discrete wavelet packet transform. The classification performance is tested on a set of 15 Brodatz textures rotated in 12 directions and for five scales across an octave. The classification performance for different wavelet filter banks for the proposed rotation and scale invariant features is tested. An application of these features for script identification is illustrated.

A Fuzzy Min-Max Neural Network Classifier With Compensatory Neuron Architecture

This paper proposes a fuzzy min-max neural network classifier with compensatory neurons (FMCNs). FMCN uses hyperbox fuzzy sets to represent the pattern classes. It is a supervised classification technique with new compensatory neuron architecture. The concept of compensatory neuron is inspired from the reflex system of human brain which takes over the control in hazardous conditions. Compensatory neurons (CNs) imitate this behavior by getting activated whenever a test sample falls in the overlapped regions amongst different classes. These neurons are capable to handle the hyperbox overlap and containment more efficiently. Simpson used contraction process based on the principle of minimal disturbance, to solve the problem of hyperbox overlaps. FMCN eliminates use of this process since it is found to be erroneous. FMCN is capable to learn the data online in a single pass through with reduced classification and gradation errors. One of the good features of FMCN is that its performance is less dependent on the initialization of expansion coefficient, i.e., maximum hyperbox size. The paper demonstrates the performance of FMCN by comparing it with fuzzy min-max neural network (FMNN) classifier and general fuzzy min-max neural network (GFMN) classifier, using several examples

Fractal dimension estimation for texture images: a parallel approach

Abstract Fractal dimension is an important parameter that can be used in various applications, such as, estimation of roughness in an image, texture segmentation, surface roughness estimation and many others. A number of techniques for fractal dimension computation in the digital domain have been reported in the literature. A parallel implementation of the Differential Box Counting technique is reported in this paper. The accuracy and computational complexity of the parallel implementation are also discussed.

Rotation invariant texture classification using even symmetric Gabor filters

Rotation invariant texture features are derived from the even symmetric Gabor filtered images of texture. The feature used is modified average absolute deviation from mean. Sixty Brodatz textures rotated in 12 different directions are classified using these features. Equal number of samples are used for training and testing phase. The percentage correct classification is 81.02. Segmentation of texture images with rotated textures is demonstrated using the features.