Texture classification and discrimination for region-based image retrieval (original) (raw)

Region based color image retrieval using curvelet transform

2010

Region based image retrieval has received significant attention from recent researches because it can provide local description of images, object based query, and semantic learning. In this paper, we apply curvelet transform to region based retrieval of color images. The curvelet transform has shown promising result in image de-noising, character recognition, and texture image retrieval. However, curvelet feature extraction for segmented regions is challenging because it requires regular (e.g., rectangular) shape images or regions, while segmented regions are usually irregular. An efficient method is proposed to convert irregular regions to regular regions. Discrete curvelet transform can then be applied on these regular shape regions. Experimental results and analyses show the effectiveness of the proposed shape transform method. We also show the curvelet feature extracted from the transformed regions outperforms the widely used Gabor features in retrieving natural color images.

Discriminative Features for Texture Retrieval Using Wavelet Packets

IEEE Access, 2019

Wavelet Packets (WPs) bases are explored seeking new discriminative features for texture indexing. The task of WP feature design is formulated as a learning decision problem by selecting the filter-bank structure of a basis (within a WPs family) that offers an optimal balance between estimation and approximation errors. To address this problem, a computationally efficient algorithm is adopted that uses the tree-structure of the WPs collection and the Kullback-Leibler divergence as a discrimination criterion. The adaptive nature of the proposed solution is demonstrated in synthetic and real data scenarios. With synthetic data, we demonstrate that the proposed features can identify discriminative bands, which is not possible with standard wavelet decomposition. With data with real textures, we show performance improvements with respect to the conventional Wavelet-based decomposition used under the same conditions and model assumptions. INDEX TERMS Texture indexing, wavelet packets, minimum probability of error, complexity regularization, minimum cost tree pruning.

Retrieval Image by Region Classification

The retrieval of images from a large database of images is an important and emerging area of research. High- resolution images in the physical database are decomposed into sets of image features which are stored in the logical database. Currently a few image retrieval systems combining color and texture as features to search images. In this paper 2D image is processed using a set of Gabor filter to derive a feature vector representing texture in the image. Color information in an image is represented by color histogram. This method is useful for processing large collections of image data.

Region-Based Image Retrieval using Wavelet Transform

Content-based image retrieval, which provides convenient ways to retrieve images from large image databases, has been studied actively. While many previous image retrieval techniques do not look at regions in an image, regionbased image retrieval techniques have been gaining attention recently. We propose a region-based image retrieval method which performs image segmentation and indexing using texture features computed from wavelet coefficients. The proposed method has advantages in texture feature extraction and hierarchical image segmentation over the previous region-based techniques using wavelet transform.

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.

Texture Classification for Content-Based Image Retrieval

An original approach to texture-based classification of regions, for image indexing and retrieval, is presented. The system addresses automatic macro-textured ROI's detection, and classification: we focus our attention on those objects that can be characterized by a texture as a whole, like trees, flowers, walls, clouds, and so on. The proposed architecture is based on the computation of the vector from each selected region, and classification of this feature by means of a pool of suitably trained Support Vector Machines (SVM's). This approach is an extension of the one previously developed by some of the authors to classify image regions on the basis of the geometrical shape of the objects they contain. Theoretical remarks, motivation of the approach, experimental setup, and the first satisfactory results on natural scenes are reported.

Color and Texture Features for Image Indexing and Retrieval

2009 IEEE International Advance Computing Conference, 2009

computed and stored to construct indexing feature The novel approach combines color and texture vectors. The Gabor wavelets are a group of features for content based image retrieval (CBIR). wavelets, with each wavelet capturing energy at a The color and texture features are obtained by specific frequency and a specific direction. computing the mean and standard deviation on Expanding a signal using this basis provides a each color band of image and sub-band of different localized frequency description, therefore wavelets. The standard Wavelet and Gabor capturing local features/energy of the signal. wavelet transforms are used for decomposing the Texture features can then be extracted from this image into sub-bands. The retrieval results group of energy distributions. The scale obtained by applying color histogram (CH) + (frequency) and orientation tunable property of Gabor wavelet transform(GWT) to a 1000 image Gabor filter makes it especially useful for database demonstrated significant improvement in constructing indexing feature vectors. precision and recall, compared to the color

Texture feature extraction in the spatial-frequency domain for content-based image retrieval

2010

The advent of large scale multimedia databases has led to great challenges in content-based image retrieval (CBIR). Even though CBIR is considered an emerging field of research, however it constitutes a strong background for new methodologies and systems implementations. Therefore, many research contributions are focusing on techniques enabling higher image retrieval accuracy while preserving low level of computational complexity. Image retrieval based on texture features is receiving special attention because of the omnipresence of this visual feature in most real-world images. This paper highlights the state-of-the-art and current progress relevant to texture-based image retrieval and spatial-frequency image representations. In particular, it gives an overview of statistical methodologies and techniques employed for texture feature extraction using most popular spatial-frequency image transforms, namely discrete wavelets, Gabor wavelets, dual-tree complex wavelet and contourlets. Indications are also given about used similarity measurement functions and most important achieved results.

Texture image retrieval using contourlet transform

2009 International Symposium on Signals, Circuits and Systems, 2009

Content based image retrieval is a challenging issue in management of existing large digital image libraries and databases. The accuracy of image retrieval methods is subject to effective extraction of image features such as color, texture, and shape. In this paper we propose a new image retrieval method using contourlet transform coefficients. We use the properties of contourlet coefficients to assign the normal distribution function to the distribution of coefficients in each sub-band. The assigned normal distribution functions are used to extract the texture feature vector at the next stage. Simulation results indicate that the proposed method outperforms other conventional texture image retrieval methods such as, Gabor filter and wavelet transform. Moreover, this method shows a noticeable higher performance compared to another contourlet based method.

Texture Image Retrieval based on Log-Gabor Features

Since Daugman found out that the properties of Gabor filters match the early psychophysical features of simple receptive fields of the Human Visual System (HVS), they have been widely used to extract texture information from images for retrieval of image data. However, Gabor filters have not zero mean, which produces a non-uniform coverage of the Fourier domain. This distortion causes fairly poor pattern retrieval accuracy. To address this issue, we propose a simple yet efficient image retrieval approach based on a novel log-Gabor filter scheme. We make emphasis on the filter design to preserve the relationship with receptive fields and take advantage of their strong orientation selectivity. We provide an experimental evaluation of both Gabor and log-Gabor features using two metrics, the Kullback-Leibler (DKL) and the Jensen-Shannon divergence (DJS). The experiments with the USC-SIPI database confirm that our proposal shows better retrieval performance than the classic Gabor features.