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.     

用于图像检索的MPEG-7形状描述子

介绍了基于内容的图像检索技术的研究现状，并对将MPEG-7形状描述子应用于图像数据库进行图像检索进行了研究，最后提出了一些需要解决的问题和今后的研究方向。     

基于Zernike矩与SIFT特征的商标检索算法

为降低商标检索算法的误检率,提出一种结合Zernike矩(ZM)和尺度不变特征变换(SIFT)的商标检索算法,该算法由离线数据库构建和在线检索组成。分别从查询图像中提取ZM和SIFT特征;根据查询图像的特征集与数据库中存储的图像的特征集之间的ZM特征进行相似度度量,形成候选商标集;最后,利用SIFT特征对查询图像与候选图像精准检测,对相似距离进行排序,将结果返回给用户。实验结果表明：与当前流行的商标检索算法相比,该算法具备更好的检索性能,在缩放、平移、模糊、透视、斜切、扭曲等变换干扰下,仍呈现出更理想的Precision- Recall曲线以及F值。     

Colour image retrieval based on primitives of colour moments

A colour image retrieval method based on the primitives of colour moments is proposed. First, an image is divided into several blocks. Then, the colour moments of all blocks are extracted and clustered into several classes. The mean moments of each class are considered as a primitive of the image. All primitives are used as features. Since two different images may have different numbers of features, a new similarity measure is then proposed. To demonstrate the effectiveness of the proposed method, two test databases from Corel are used to compare the performances of the proposed method with other existing ones. The experimental results show that the proposed method is usually better than the others. Furthermore, since for a few special types of images, other methods may have better results occasionally, a relevance feedback algorithm is also provided to automatically determine the best method according to the user's response.     

Exact Zernike and pseudo-Zernike moments image reconstruction based on circular overlapping blocks and Chamfer distance

This study aims to explore a novel approach to reconstruct multi-gray-level images based on circular blocks reconstruction method using two exact and fast moments: Zernike (CBR-EZM) and pseudo-Zernike (CBR-EPZM): An image is first divided into a set of sub-images which are then reconstructed independently. We also introduced Chamfer distance (CD) to capitalize on the use of discrete distance instead of Euclidean one. The combination of our methods and CD leads to CBR-EZM-CD and CBR-EPZM-CD methods. Obviously, image partitioning offers significant advantages, but an undesirable circular blocking effect can occur. To mitigate this effect, we have implemented overlapping feature to our new methods leading to OCBR-EZM-CD and OCBR-EPZM-CD, by exploiting neighborhood information of the circular blocks. The main motivation of this novel approach is to explore new applications of Zernike and pseudo-Zernike moments. One of the fields is feature extraction for pattern recognition: Zernike and pseudo-Zernike moments are well known to capture only the global features, but thanks to the circular block reconstruction, we can now use those moments to extract also local features.     

Integration of wavelet transform,Local Binary Patterns and moments for content-based image retrieval

The proliferation of large number of images has made it necessary to develop systems for indexing and organizing images for easy access. This has made Content-Based Image Retrieval (CBIR) an important area of research in Computer Vision. This paper proposes a combination of features in multiresolution analysis framework for image retrieval. In this work, the concept of multiresolution analysis has been exploited through the use of wavelet transform. This paper combines Local Binary Pattern (LBP) with Legendre Moments at multiple resolutions of wavelet decomposition of image. First, LBP codes of Discrete Wavelet Transform (DWT) coefficients of images are computed to extract texture feature from image. The Legendre Moments of these LBP codes are then computed to extract shape feature from texture feature for constructing feature vectors. These feature vectors are used to search and retrieve visually similar images from large database. The proposed method has been tested on five benchmark datasets, namely, Corel-1K, Olivia-2688, Corel-5K, Corel-10K, and GHIM-10K, and performance of the proposed method has been measured in terms of precision and recall. The experimental results demonstrate that the proposed method outperforms some of the other state-of-the-art methods in terms of precision and recall.     

Zernike矩不变性分析及其改进

在分析了Zernike矩不变性后,提出了一种基于图像整个区域形状描述算子的改进Zernike矩。改进的Zernike矩不仅具有旋转不变性,而且还具有改进前不具有的比例不变性。试验结果证明,和改进前的Zernike矩相比,改进后的Zernike矩具有更好的不变特征,可用作基于区域的形状描述算子。     

A unified framework for image retrieval using keyword and visual features.

In this paper, a unified image retrieval framework based on both keyword annotations and visual features is proposed. In this framework, a set of statistical models are built based on visual features of a small set of manually labeled images to represent semantic concepts and used to propagate keywords to other unlabeled images. These models are updated periodically when more images implicitly labeled by users become available through relevance feedback. In this sense, the keyword models serve the function of accumulation and memorization of knowledge learned from user-provided relevance feedback. Furthermore, two sets of effective and efficient similarity measures and relevance feedback schemes are proposed for query by keyword scenario and query by image example scenario, respectively. Keyword models are combined with visual features in these schemes. In particular, a new, entropy-based active learning strategy is introduced to improve the efficiency of relevance feedback for query by keyword. Furthermore, a new algorithm is proposed to estimate the keyword features of the search concept for query by image example. It is shown to be more appropriate than two existing relevance feedback algorithms. Experimental results demonstrate the effectiveness of the proposed framework.     

Using Zernike moments for the illumination and geometry invariantclassification of multispectral texture

We develop a method for recognizing color texture independent of rotation, scale, and illumination. Color texture is modeled using spatial correlation functions defined within and between sensor bands. Using a linear model for surface spectral reflectance with the same number of parameters as the number of sensor classes, we show that illumination and geometry changes in the scene correspond to a linear transformation of the correlation functions and a linear transformation of their coordinates. A several step algorithm that includes scale estimation and correlation moment computation is used to achieve the invariance. The key to the method is the new result that illumination, rotation, and scale changes in the scene correspond to a specific transformation of correlation function Zernike moment matrices. These matrices can be estimated from a color image. This relationship is used to derive an efficient algorithm for recognition. The algorithm is substantiated using classification results on over 200 images of color textures obtained under various illumination conditions and geometric configurations.     

Region-based image retrieval using color-size features of watershed regions

This paper presents a region-based image retrieval system that provides a user interface for helping to specify the watershed regions of interest within a query image. We first propose a new type of visual features, called color-size feature, which includes color-size histogram and moments, to integrate color and region-size information of watershed regions. Next, we design a scheme of region filtering that is based on color-size histogram to fast screen out some of most irrelevant regions and images for the preprocessing of the image retrieval. Our region-based image retrieval system applies the Earth Mover’s Distance in the design of the similarity measure for image ranking and matching. Finally, we present some experiments for the color-size feature, region filtering, and retrieval results that demonstrate the efficiency of our proposed system.     

A new Zernike moments based technique for camera identification and forgery detection

In multimedia forensics, it is important to identify those images that were captured by a specific camera from a given set of N data images as well as detecting the tampered region in these images if forged. This paper presents a new technique based on Zernike moments feature extraction for blindly classifying correlated PRNU images as well as locating the tampered regions in image under investigation. The proposed clustering algorithm is based on estimating the Zernike moments and applying a hierarchical clustering for classification. The forgery detection algorithm is based on picking up the peak Euclidean distance between the Zernike moments vector of blocks of the scaled-down forged image and its corresponding ones in the capturing camera PRNU. As Zernike moments are scale and rotational invariant, its feature when computed using scaled-down PRNU images lead to considerable computation time saving. Simulation examples are given to verify the effectiveness of the proposed techniques when compared to other state-of-the-art techniques even in case of very weakly correlated PRNU.     

Content-based image retrieval using a fusion of global and local features

Color, texture, and shape act as important information for images in human recognition. For content-based image retrieval, many studies have combined color, texture, and shape features to improve the retrieval performance. However, there have not been many powerful methods for combining all color, texture, and shape features. This study proposes a content-based image retrieval method that uses the combined local and global features of color, texture, and shape. The color features are extracted from the color autocorrelogram; the texture features are extracted from the magnitude of a complete local binary pattern and the Gabor local correlation revealing local image characteristics; and the shape features are extracted from singular value decomposition that reflects global image characteristics. In this work, an experiment is performed to compare the proposed method with those that use our partial features and some existing techniques. The results show an average precision that is 19.60% higher than those of existing methods and 9.09% higher than those of recent ones. In conclusion, our proposed method is superior over other methods in terms of retrieval performance.     

一种新的综合颜色和纹理特征的图像检索方法

提出综合HSV颜色直方图和Gabor小波纹理特征进行检索的新方法,既利用颜色特征对图像颜色全局分布的描述,又利用纹理特征对局部空间信息的描述,避免一种特征描述图像的片面性。基于Corel库的检索实验结果表明,该方法可以取得良好的检索效果。