The new integrated color and texture based image retrieval using neuro-fuzzy approach
22 Jul 2009-pp 1-8
TL;DR: In this paper, a color and texture based neural network -fuzzy logic approach for content based image retrieval using 2D-wavelet transform was proposed, which improved the retrieval performance by learning and searching capability of the neural network combined with the fuzzy interpretation.
Abstract: In this paper we introduce the new integrated color and texture based image retrieval technique using neuro-fuzzy approach for content based image retrieval. Most of the image retrieval systems are still incapable of providing retrieval result with high retrieval accuracy and less computational complexity. To address this problem, we developed color and texture based neural network -fuzzy logic approach for content based image retrieval using 2D-wavelet transform. The system performance improved by the learning and searching capability of the neural network combined with the fuzzy interpretation. This overcomes the vagueness and inconsistency due to human subjectivity. Multiresolution analysis using 2D-DWT can decompose the image into components at different scales, so that the coarest scale components carry the global approximation information while the finer scale components contain the detailed information. The empirical results show that the precision improved from 67% to 98% and average recall rate of 67% to 98% for the general purpose database size of 10000 images compared with existing approaches.
21 Oct 2013
TL;DR: A new fuzzy based approach for word level script identification of text in low resolution images of display boards is presented that is robust and insensitive to the variations in size and style of font, number of characters, thickness and spacing between characters, noise, and other degradations.
Abstract: Automated systems for understanding low resolution images of display boards are facilitating several new applications such as blind assistants, tour guide systems, location aware systems and many more. Script identification at character/word level is one of the very important pre-processing steps for development of such systems prior to further image analysis. In this paper, a new fuzzy based approach for word level script identification of text in low resolution images of display boards is presented. The proposed methodology uses horizontal run statistics and wavelet features for distinguishing 5 Indian scripts namely; Hindi, Kannada, English, Malyalam and Tamil. The method works in two phases; In the first phase, the wavelet transform based texture features such as zone wise wavelet energy features, vertical run statistical features of wavelet coefficients and wavelet log mean deviation features of decomposed energy bands at 2 levels are obtained from training word images and crisp sets are constructed, one for each script/language under study. The second phase is testing, in which test word image is processed to obtain horizontal run statistics to determine whether it belongs to Hindi script. Otherwise, the word image is processed to obtain a crisp vector. The degree of belongingness of crisp vector with each candidate object in the crisp sets is determined using newly devised fuzzy membership function. Further, fuzzy inference scheme is used to identify the script of the test word image. The proposed method is robust and insensitive to the variations in size and style of font, number of characters, thickness and spacing between characters, noise, and other degradations. The proposed method achieves an overall script identification accuracy of 94.33% and individual identification accuracy of 100% for Hindi Script, 98.67% for Kannada Script, 100% for English, 89% for Malyalam and 84% for Tamil Script.
••01 Nov 1973
TL;DR: These results indicate that the easily computable textural features based on gray-tone spatial dependancies probably have a general applicability for a wide variety of image-classification applications.
Abstract: Texture is one of the important characteristics used in identifying objects or regions of interest in an image, whether the image be a photomicrograph, an aerial photograph, or a satellite image. This paper describes some easily computable textural features based on gray-tone spatial dependancies, and illustrates their application in category-identification tasks of three different kinds of image data: photomicrographs of five kinds of sandstones, 1:20 000 panchromatic aerial photographs of eight land-use categories, and Earth Resources Technology Satellite (ERTS) multispecial imagery containing seven land-use categories. We use two kinds of decision rules: one for which the decision regions are convex polyhedra (a piecewise linear decision rule), and one for which the decision regions are rectangular parallelpipeds (a min-max decision rule). In each experiment the data set was divided into two parts, a training set and a test set. Test set identification accuracy is 89 percent for the photomicrographs, 82 percent for the aerial photographic imagery, and 83 percent for the satellite imagery. These results indicate that the easily computable textural features probably have a general applicability for a wide variety of image-classification applications.
TL;DR: In this paper, it is shown that the difference of information between the approximation of a signal at the resolutions 2/sup j+1/ and 2 /sup j/ (where j is an integer) can be extracted by decomposing this signal on a wavelet orthonormal basis of L/sup 2/(R/sup n/), the vector space of measurable, square-integrable n-dimensional functions.
Abstract: Multiresolution representations are effective for analyzing the information content of images. The properties of the operator which approximates a signal at a given resolution were studied. It is shown that the difference of information between the approximation of a signal at the resolutions 2/sup j+1/ and 2/sup j/ (where j is an integer) can be extracted by decomposing this signal on a wavelet orthonormal basis of L/sup 2/(R/sup n/), the vector space of measurable, square-integrable n-dimensional functions. In L/sup 2/(R), a wavelet orthonormal basis is a family of functions which is built by dilating and translating a unique function psi (x). This decomposition defines an orthogonal multiresolution representation called a wavelet representation. It is computed with a pyramidal algorithm based on convolutions with quadrature mirror filters. Wavelet representation lies between the spatial and Fourier domains. For images, the wavelet representation differentiates several spatial orientations. The application of this representation to data compression in image coding, texture discrimination and fractal analysis is discussed. >
•01 May 1992
TL;DR: This paper presents a meta-analyses of the wavelet transforms of Coxeter’s inequality and its applications to multiresolutional analysis and orthonormal bases.
Abstract: Introduction Preliminaries and notation The what, why, and how of wavelets The continuous wavelet transform Discrete wavelet transforms: Frames Time-frequency density and orthonormal bases Orthonormal bases of wavelets and multiresolutional analysis Orthonormal bases of compactly supported wavelets More about the regularity of compactly supported wavelets Symmetry for compactly supported wavelet bases Characterization of functional spaces by means of wavelets Generalizations and tricks for orthonormal wavelet bases References Indexes.
TL;DR: The working conditions of content-based retrieval: patterns of use, types of pictures, the role of semantics, and the sensory gap are discussed, as well as aspects of system engineering: databases, system architecture, and evaluation.
Abstract: Presents a review of 200 references in content-based image retrieval. The paper starts with discussing the working conditions of content-based retrieval: patterns of use, types of pictures, the role of semantics, and the sensory gap. Subsequent sections discuss computational steps for image retrieval systems. Step one of the review is image processing for retrieval sorted by color, texture, and local geometry. Features for retrieval are discussed next, sorted by: accumulative and global features, salient points, object and shape features, signs, and structural combinations thereof. Similarity of pictures and objects in pictures is reviewed for each of the feature types, in close connection to the types and means of feedback the user of the systems is capable of giving by interaction. We briefly discuss aspects of system engineering: databases, system architecture, and evaluation. In the concluding section, we present our view on: the driving force of the field, the heritage from computer vision, the influence on computer vision, the role of similarity and of interaction, the need for databases, the problem of evaluation, and the role of the semantic gap.
TL;DR: Comparisons with other multiresolution texture features using the Brodatz texture database indicate that the Gabor features provide the best pattern retrieval accuracy.
Abstract: Image content based retrieval is emerging as an important research area with application to digital libraries and multimedia databases. The focus of this paper is on the image processing aspects and in particular using texture information for browsing and retrieval of large image data. We propose the use of Gabor wavelet features for texture analysis and provide a comprehensive experimental evaluation. Comparisons with other multiresolution texture features using the Brodatz texture database indicate that the Gabor features provide the best pattern retrieval accuracy. An application to browsing large air photos is illustrated.