Local tetra patterns: a new feature descriptor for content-based image retrieval

In this paper, we propose a novel image indexing and retrieval algorithm using local tetra patterns (LTrPs) for content-based image retrieval (CBIR). The standard local binary pattern (LBP) and local ternary pattern (LTP) encode the relationship between the referenced pixel and its surrounding neighbors by computing gray-level difference. The proposed method encodes the relationship between the referenced pixel and its neighbors, based on the directions that are calculated using the first-order derivatives in vertical and horizontal directions. In addition, we propose a generic strategy to compute nth-order LTrP using (n - 1)th-order horizontal and vertical derivatives for efficient CBIR and analyze the effectiveness of our proposed algorithm by combining it with the Gabor transform. The performance of the proposed method is compared with the LBP, the local derivative patterns, and the LTP based on the results obtained using benchmark image databases viz., Corel 1000 database (DB1), Brodatz texture database (DB2), and MIT VisTex database (DB3). Performance analysis shows that the proposed method improves the retrieval result from 70.34%/44.9% to 75.9%/48.7% in terms of average precision/average recall on database DB1, and from 79.97% to 85.30% and 82.23% to 90.02% in terms of average retrieval rate on databases DB2 and DB3, respectively, as compared with the standard LBP.     

Local maximum edge binary patterns: A new descriptor for image retrieval and object tracking

A new algorithm meant for content based image retrieval (CBIR) and object tracking applications is presented in this paper. The local region of image is represented by local maximum edge binary patterns (LMEBP), which are evaluated by taking into consideration the magnitude of local difference between the center pixel and its neighbors. This LMEBP differs from the existing LBP in a manner that it extracts the information based on distribution of edges in an image. Further, the effectiveness of our algorithm is confirmed by combining it with Gabor transform. Four experiments have been carried out for proving the worth of our algorithm. Out of which three are meant for CBIR and one for object tracking. It is further mentioned that the database considered for first three experiments are Brodatz texture database (DB1), MIT VisTex database (DB2), rotated Brodatz database (DB3) and the fourth contains three observations. The results after being investigated show a significant improvement in terms of their evaluation measures as compared to LBP and other existing transform domain techniques.     

A novel local octa-pattern feature descriptor for image retrieval

The local tetra patterns (LTrPs) gives four-directional information and ignores the diagonal pixel information, thereby affecting the retrieved image efficiency. In the present work, a novel retrieval approach has been proposed using local octa-patterns (LOcPs) for content-based image indexing and retrieval. The proposed approach encodes the center pixel directional information with its eight adjacent neighbors, from the directions that are computed using the first-order derivatives. Also the nth-order LOcP is computed using \((n-1)\)th-order local direction variations. In addition, the performance of the developed method by combining it with the Gabor transform has been analyzed. The performance of the proposed technique has been compared to existing techniques like LBP, LTP, LDP, and LTrP on Corel-1000 database (DB1) and Describable Textures Dataset (DB2). The performance observed shows that the developed method improves the retrieval parameters from 75.9%/77.13% to 79.4%/81.5% in the form of average precision on DB1/DB2 databases.     

Center symmetric local binary co-occurrence pattern for texture,face and bio-medical image retrieval

Content based image retrieval is a common problem for a large image database. Many methods have been proposed for image retrieval for some particular type of datasets. In the proposed work, a new image retrieval technique has been introduced. This technique is useful for different kind of dataset. In the proposed method, center symmetric local binary pattern has been extracted from the original image to obtain the local information. Co-occurrence of pixel pairs in local pattern map have been observed in different directions and distances using gray level co-occurrence matrix. Earlier methods have utilized histogram to extract the frequency information of local pattern map but co-occurrence of pixel pairs is more robust than frequency of patterns. The proposed method is tested on three different category of images, i.e., texture, face and medical image database and compared with typical state-of-the-art local patterns.     

Content based image indexing and retrieval using directional local extrema and magnitude patterns

In this paper, we integrate the concept of directional local extremas and their magnitude based patterns for content based image indexing and retrieval. The standard ditectional local extrama pattern (DLEP) extracts the directional edge information based on local extrema in 0°, 45°, 90°, and 135° directions in an image. However, they are not considering the magnitudes of local extremas. The proposed method integrates these two concepts for better retrieval performance. The sign DLEP (SDLEP) operator is a generalized DLEP operator and magnitude DLEP (MDLEP) operator is calculated using magnitudes of local extremas. The performance of the proposed method is compared with DLEP, local binary patterns (LBPs), block-based LBP (BLK_LBP), center-symmetric local binary pattern (CS-LBP), local edge patterns for segmentation (LEPSEG) and local edge patterns for image retrieval (LEPINV) methods by conducting two experiments on benchmark databases, viz. Corel-5K and Corel-10K databases. The results after being investigated show a significant improvement in terms of their evaluation measures as compared to other existing methods on respective databases.     

基于局部边缘二值模式的图像检索

在定义局部边缘的基础上提出了局部边缘二值模式(LEBP),并结合Gabor滤波器将其扩展到多分辨率LEBP(MLEBP)。对传统的中心对称局部二值模式(CS-LBP)和方向局部二值模式(D-LBP)进行了改进,新描述符在不增加计算复杂度和提高特征维数的基础上,进一步融入了局部边缘信息。为验证新描述符的性能,采用3个通用的纹理图像库进行图像检索实验。结果表明,结合本文方法,明显提高了传统描述符的分辨能力。     

融合统一模式的LBP和GLCM特征的图像检索算法

文章提出一种融合统一模式的LBP特征和GLCM特征的图像检索算法。首先,对图像做统一模式的LBP变换处理提取图像直方图特征。计算图像GLCM在0°、45°、90°、135°四个方向下的对比度、相关性、能量、同质系数的均值和标准差。然后,融合LBP直方图特征与GLCM纹理统计特征作为图像的检索特征,选用Canberra距离进行相似度度量,完成图像检索。实验结果表明,本算法在两个图像库上均具有较高的平均查准率,分别达到了82.91%和79.48%。     

基于模糊三值模式的矿物浮选泡沫图像边缘检测方法

针对一类边缘特征不明显的矿物浮选泡沫图像,提出了一种基于模糊三值模式的泡沫图像边缘检测方法.在‘0/1’二值模式基础上,增加不确定逻辑状态,构成模糊局部三值模式,以描述邻域像素灰度均值的不确定关系,同时,对邻域双向灰度差值之和进行模糊化,以描述边缘与非边缘方向的关系,联立邻域灰度关系与双向灰度差值隶属度,构造气泡边缘隶属度矩阵,依据联合隶属度的解模糊结果判决是否为边界候选像素,再根据边界候选像素集合的特征剔除非边界像素,以此得到泡沫边缘.实验结果表明,该方法能够有效地检测出气泡边缘,同时,在强噪声环境下,具有良好的鲁棒性.     

Expert content-based image retrieval system using robust local patterns

A new image indexing and retrieval algorithm for content based image retrieval is proposed in this paper. The local region of the image is represented by making the use of local difference operator (LDO), separating it into two components i.e. sign and magnitude. The sign LBP operator (S_LBP) is a generalized LBP operator. The magnitude LBP (M_LBP) operator is calculated using the magnitude of LDO. A robust LBP (RLBP) operator is presented employing robust S_LBP and robust M_LBP. Further, the combination of Gabor transform and RLBP operator has also been presented. The robustness is established by conducting four experiments on different image database i.e. Corel 1000 (DB1), Brodatz texture database (DB2) and MIT VisTex database (DB3) under different lighting (illumination) and noise conditions. Investigations reveal a promising achievement of the technique presented when compared to S_LBP and other existing transform domain techniques in terms of their evaluation measures.     

Knowledge-driven multidimensional indexing structure for biomedical media database retrieval.

Today, biomedical media data are being generated at rates unimaginable only years ago. Content-based retrieval of biomedical media from large databases is becoming increasingly important to clinical, research, and educational communities. In this paper, we present the recently developed entropy balanced statistical (EBS) k-d tree and its applications to biomedical media, including a high-resolution computed tomography (HRCT) lung image database and the first real-time protein tertiary structure search engine. Our index utilizes statistical properties inherent in large-scale biomedical media databases for efficient and accurate searches. By applying concepts from pattern recognition and information theory, the EBS k-d tree is built through top-down decision tree induction. Experimentation shows similarity searches against a protein structure database of 53 363 structures consistently execute in less than 8.14 ms for the top 100 most similar structures. Additionally, we have shown improved retrieval precision over adaptive and statistical k-d trees. Retrieval precision of the EBS k-d tree is 81.6% for content-based retrieval of HRCT lung images and 94.9% at 10% recall for protein structure similarity search. The EBS k-d tree has enormous potential for use in biomedical applications embedded with ground-truth knowledge and multidimensional signatures.     

改进邻域平均法在MRI图像去噪处理中的研究

对磁共振成像图像的去噪声处理是医学图像预处理中的重要环节。为了克服均值滤波算法不能兼顾去噪和保持图像边缘细节的不足,提出了改进邻域平均法。先根据相似者相容的原理得到待像素与各模板的关系,从而判断该像素是否为孤立噪声点。然后对孤立噪声点用灰度相近T邻域平均法进行处理,并对非孤立噪声点采用加权平滑模板进行处理。实验结果表明,该算法在去除噪声的同时,还保留了图像的边缘和细节。     

Efficient Cloud Image Retrieval System Using Weighted-Inverted Index and Database Filtering Algorithms

With the advance of multimedia technology and communications, images and videos become the major streaming information through the Internet. How to fast retrieve desired similar images precisely from the Internet scale image/video databases is the most important retrieval control target. In this paper, a cloud based content-based image retrieval (CBIR) scheme is presented. Database-categorizing based on weighted-inverted index (DCWⅡ) and database filtering algorithm (DFA) is used to speed up the features matching process. In the DCWⅡ, the weights are assigned to discrete cosine transform (DCT) coefficients histograms and the database is categorized by weighted features. In addition, the DFA filters out the irrelevant image in the database to reduce unnecessary computation loading for features matching. Experiments show that the proposed CBIR scheme outperforms previous work in the precision-recall performance and maintains mean average precision (mAP) about 0.678 in the large-scale database comprising one million images. Our scheme also can reduce about 50% to 85% retrieval time by pre-filtering the database, which helps to improve the efficiency of retrieval systems.     

Zernike moments and LDP-weighted patches for content-based image retrieval

This paper proposes a new algorithm using global and local features for content-based image retrieval. Global features are extracted using the magnitude of Zernike moments (ZMs). Local features are obtained through local directional pattern (LDP). Generally, LDP is used to extract texture-based features from an image. In this paper, LDP is used to encode both texture and shape information of an image to represent more meaningful features. To encode texture-based features, original image is used to compute the LDP features. To extract shape information from an image, dual-tree complex wavelet transform (DT-CWT) is applied on image which generates six directional wavelets. These six directional wavelets are superimposed in order to obtain shape-encoded image. LDP is then applied on this wavelet-based shape-encoded image. Further, to enhance retrieval accuracy, LDP features are extracted from patches of both original and shape-encoded images. These patches are assigned with weights based on average discrimination capability of features in a patch. Experiments are performed using three different standard databases with various variations such as pose, distortion, partial occlusion and complex structure. The proposed technique achieves 96.4 and 98.76 % retrieval accuracy at a recall of 50 %, for Kimia-99 and COIL-100 databases, respectively. For MPEG-7 CE-2 shape database, retrieval accuracy of 61.93 % is achieved in terms of average Bull’s eye performance (BEP). The proposed technique is also tested on Springer medical image database to explore its scope in other areas, wherein it attains average BEP of 69.68 % in comparison with 61.52 % with ZMs. It is observed that the proposed technique outperforms other well-known existing methods of image retrieval.