Texture image retrieval using adaptive tetrolet transforms

This paper proposes a novel technique for texture image retrieval based on tetrolet transforms. Tetrolets provide fine texture information due to its different way of analysis. Tetrominoes are applied at each decomposition level of an image and best combination of tetrominoes is selected, which better shows the geometry of an image at each level. All three high pass components of the decomposed image at each level are used as input values for feature extraction. A feature vector is created by taking standard deviation in combination with energy at each subband. Retrieval performance in terms of accuracy is tested on group of texture images taken from benchmark databases: Brodatz and VisTex. Experimental results indicate that the proposed method achieves 78.80% retrieval accuracy on group of texture images D1 (taken from Brodatz), 84.41% on group D2 (taken from VisTex) and 77.41% on rotated texture image group D3 (rotated images from Brodatz).     

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 degrees 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.     

Content-based image retrieval using local ternary wavelet gradient pattern

Multimedia Tools and Applications - With the invention of low cost image capturing devices, image acquisition is no longer a difficult task. The immense popularity of such devices has led to the...     

基于互补特征的纹理图像检索

针对互补特征可以有效地改善图像检索系统性能的特点,提出一种在改进Contourlet变换域采用L1能量与广义高斯分布参数特征的纹理图像检索方法。首先,应用改进的方法对方向子带系数进行广义高斯统计建模。然后,分别单独利用各个特征和相应的相似性测度进行检索。最后,基于直接的相似性测度和,采用这两种互补的特征进行检索。实验结果表明,和采用单一特征相比较,互补特征由于充分地反映了图像的结构信息和随机分布信息,从而有效地提高了纹理图像数据库的平均检索率。     

Biomedical image retrieval by using local directional edge binary patterns and Zernike moments

Multimedia Tools and Applications - This paper presents an efficient approach with the reduced length of feature vector for biomedical image retrieval by using global and local features of an...     

Microstructure pattern extraction based image retrieval

Computer vision techniques enhanced by the advent of deep learning has become a quintessential part of our day-to-day life. The application of such computer vision techniques in image retrieval can be termed as query based image retrieval process. Conventional methods have limitations such as increased dimensionality, reduced accuracy, high time consumption, and dependence on indexing for retrieval. In order to overcome these limitations, this research work aims to develop a new image retrieval system by developing an image preprocessing mechanism via target prediction technique, which isolates object from the background. Further, a Micro-structure based Pattern Extraction (MPE) technique is implemented to extract the patterns from the preprocessed image, where the diagonal patterns are generated for increasing the accuracy of the retrieval process. Consequently, the Convolutional Neural Network (CNN) is utilized to reduce the dimensionality of the features, and the similarity learning approach is utilized to map the selected features with trained features based on the distance metric. The performance of the proposed system is evaluated by using various measures. Thereby, the efficiency of the proposed technique is ascertained by comparing it with the existing techniques.

     

Texture-based pattern retrieval from image databases

Recent technological advances have made it possible to process and store large amounts of image data. Perhaps the most impressive example is the accumulation of image data in scientific applications such as medical or satellite imagery. However, in order to realize their full potential, tools for efficient extraction of information and for intelligent searches in image databases need to be developed. This paper describes a new approach to image data retrieval which allows queries to be composed of local intensity patterns. The intensity pattern is converted into a feature representation of reduced dimensionality which can be used for searching similar-looking patterns in the database. This representation is obtained by filtering the pattern with a bank of scale and orientation selective filters modeled using Gabor functions. Experimental results are presented which illustrate that the proposed representation preserves the perceptual similarities, and provides a powerful tool for content-based image retrieval.     

基于方向导数纹理描述子的纹理图像聚类

在深入分析CS—LBP、ICS—LBP、FCS—LBP、ECS—LBP的基础上,提出了一种4维的快速纹理描述子。该描述子计算4个方向的方向导数,在维数由CS—LBP、ICS—LBP、FCS—LBP的16维降到了4维的同时,较CS—LBP、ICS—LBP、FCS—LBP、ECS—LBP具有更好的区分能力,对纹理图像的聚类准确度更高,描述子计算时间平均在CS—LBP、ICS—LBP、FCS—LBP、ECS—LBP的以内。     

Texture features for browsing and retrieval of image data

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     

利用脉冲耦合神经网络的纹理图像检索方法

为提高基于内容的图像检索系统(CBIR)中纹理特征提取的有效性,进一步提升CBIR系统的整体性能。提出了一种基于脉冲耦合神经网络的纹理图像检索方法。脉冲耦合神经网络(PCNN)是新一代的人工神经网络,在数据处理上具有很多优势。特征提取时具有平移、旋转、尺度、扭曲等不变性,以及很好的抗噪性,而这一点非常适合于图像检索系统。利用PCNN及简化模型ICM得到对应于不同灰度值的二值图像序列,计算序列中每幅图像的熵序列,其一维的特征矢量作为纹理特征。采用Eu-clidean距离进行相似度计算,建立了一套基于示例查询图像的纹理图像检索系统。实验结果表明,与小波包等特征提取方法相比,该方法不仅对噪声具有较强的鲁棒性,同时能降低特征向量维数,具有尺度、平移和旋转不变性,而且能取得更高的检索率。     

Texture pattern image generation by regular Markov chain

A method of graytone texture pattern generation using a regular Markov chain is presented. The procedure arranges the generated gray tones in a sequence along a scan line. The transition probability matrix of the Markov chain directly determines the spatial co-occurrence probabilities of gray tones in the generated image. The generated image can be rotated and arithmetically combined to produce images of additional texture patterns. The paper illustrates the variety of texture which can be produced by this method.     

一种基于轮廓提取与纹理分析的图像着色方法*

从分析卡通、漫画图像的构图方法入手提出一种基于颜色扩展的图像着色方法,通过提取图像轮廓边界形成着色方向场指导上色过程,利用Gabor小波实现对图像中非相邻纹理区域的分析与匹配.实验结果说明该方法通过简洁的色彩关系和明暗层次突出了卡通、漫画图像适度简化和夸张的艺术特点,不相邻区域纹理匹配有效简化了细节部分添加色彩标记的用户交互复杂度.     

Multi-channel local ternary pattern for content-based image retrieval

Pattern Analysis and Applications - A feature based on a single modality such as color or texture is not sufficient to investigate the appearance variation across multiple images. In this paper, a...     

Texture image retrieval based on fusion of local and global features

Neither a single local feature nor a single global feature can completely characterize image information, and fusion of two or more complementary features can effectively improve retrieval performance in image retrieval. In this paper, a texture image retrieval method is proposed by fusing global and local features in the spatial domain and the transform domain. In the spatial domain, the local binary pattern (LBP) value of the image is calculated, and the histogram is established as the feature. In the transform domain, the dual-tree complex wavelet transform (DTCWT) is selected to decompose the image into sub-bands, in which the low-frequency approximate sub-band coefficients are modeled by Gaussian Mixture Model (GMM), magnitude sub-band coefficients are modeled by Gamma distribution model, and relative phase sub-band coefficients are modeled by von Mises distribution model; the LBP value of the magnitude sub-band coefficients and the improved local tetra pattern(ILTrP) value of the relative phase sub-band coefficients are calculated. According to the influence of different types of features on retrieval performance, the optimized weight coefficient is set for each type of feature, and accordingly a new similarity measurement formula is proposed. The experimental results on three different image databases of Brodatz database (DB1), MIT VisTex database (DB2) and STex (DB3) show that the average retrieval rate (ARR) of our method for databases DB1, DB2, and DB3 reaches 84.32%, 90.43% and 64.73%, respectively; and compared with the state-of-the-art methods, the ARR in DB1 increases by 1.04%, in DB2 by 0.35%, and in DB3 by 1.68%.

     

Line pattern retrieval using relational histograms

This paper presents a new compact shape representation for retrieving line-patterns from large databases. The basic idea is to exploit both geometric attributes and structural information to construct a shape histogram. We realize this goal by computing the N-nearest neighbor graph for the lines-segments for each pattern. The edges of the neighborhood graphs are used to gate contributions to a two-dimensional pairwise geometric histogram. Shapes are indexed by searching for the line-pattern that maximizes the cross correlation of the normalized histogram bin-contents. We evaluate the new method on a database containing over 2,500 line-patterns each composed of hundreds of lines