Robust image hash function using local color features

Conventional image hash functions only exploit luminance components of color images to generate robust hashes and then lead to limited discriminative capacities. In this paper, we propose a robust image hash function for color images, which takes all components of color images into account and achieves good discrimination. Firstly, the proposed hash function re-scales the input image to a fixed size. Secondly, it extracts local color features by converting the RGB color image into HSI and YCbCr color spaces and calculating the block mean and variance from each component of the HSI and YCbCr representations. Finally, it takes the Euclidian distances between the block features and a reference feature as hash values. Experiments are conducted to validate the efficiency of our hash function. Receiver operating characteristics (ROC) curve comparisons with two existing algorithms demonstrate that our hash function outperforms the assessed algorithms in classification performances between perceptual robustness and discriminative capability.     

基于内容视频信息检索系统的分析研究

介绍了基于内容视频检索系统所涉及的主要技术和工作原理,对目前国外几个典型视频检索系统进行了分析,指出了存在的问题及相关的解决方案,并就今后如何进行基于内容视频检索系统的研究提出了一些新的看法.     

一种基于本体的语义检索设计与实现

基于语义检索的目的,结合成语典故本体的构建,设计了一个基于成语典故本体的语义检索模型,阐述了检索模型中用户界面模块、数据存储模块、查询分析模块和检索分析模块的功能;分析了系统中的本体构建技术、分词技术及检索技术,设计并实现了词语相似度及概念相似度算法;通过语义检索系统的实验,得到较高的查全率和查准率.     

云参数和可降水总量的同步物理反演方法试验

利用改进的同步物理反演方法计算东亚地区的云参数和可降水总量,并用气象卫星短、长波窗区通道亮度温度差对反演结果进行真实性检验.结果表明:该反演算法的效果是好的.     

Robust common visual pattern discovery using graph matching

Discovering common visual patterns (CVPs) between two images is a difficult and time-consuming task, due to the photometric and geometric transformations. The state-of-the-art methods for CVPs discovery are either computationally expensive or have complicated constraints. In this paper, we formulate CVPs discovery as a graph matching problem, depending on pairwise geometric compatibility between feature correspondences. To efficiently find all CVPs, we propose a novel framework which consists of three components: Preliminary Initialization Optimization (PIO), Guided Expansion (GE) and Post Agglomerative Combination (PAC). PIO gets the initial CVPs and reduces the search space of CVPs discovery, based on the internal homogeneity of CVPs. Then, GE anchors on the initializations and gradually explores them, to find more and more correct correspondences. Finally, to reduce false and miss detection, PAC refines the discovery result in an agglomerative way. Experiments and applications conducted on benchmark datasets demonstrate the effectiveness and efficiency of our method.     

SRI-Net: Similarity retrieval-based inference network for light field salient object detection

The cutting-edge RGB saliency models are prone to fail for some complex scenes, while RGB-D saliency models are often affected by inaccurate depth maps. Fortunately, light field images can provide a sufficient spatial layout depiction of 3D scenes. Therefore, this paper focuses on salient object detection of light field images, where a Similarity Retrieval-based Inference Network (SRI-Net) is proposed. Due to various focus points, not all focal slices extracted from light field images are beneficial for salient object detection, thus, the key point of our model lies in that we attempt to select the most valuable focal slice, which can contribute more complementary information for the RGB image. Specifically, firstly, we design a focal slice retrieval module (FSRM) to choose an appropriate focal slice by measuring the foreground similarity between the focal slice and RGB image. Secondly, in order to combine the original RGB image and the selected focal slice, we design a U-shaped saliency inference module (SIM), where the two-stream encoder is used to extract multi-level features, and the decoder is employed to aggregate multi-level deep features. Extensive experiments are conducted on two widely used light field datasets, and the results firmly demonstrate the superiority and effectiveness of the proposed SRI-Net.     

基于KL距离和双密度小波变换的纹理图像检索

为了进一步提纹理图像的检索性能，提出了一种基于双密度小波的算法。该算法根据双密度小波分解的特点。从系数角度出发首先进行子带组合，然后提取子带小波系数直方图分布特性作为纹理特征。利用最大似然估计规则将特征提取和相似计算结合起来．采用KL距离进行度量．与单小波和双密度小波方法比较．该算法具有时移不变性、特证数少等特点。理论分析和纹理图像检索的对比实验数据说明了组合双密度小波在纹理特征提取方面的性能优于单小波和双密度小波。检索率分别提高了。     

星载合成孔径雷达海面风速反演技术研究

CMOD系列地球物理模式函数作为业务化应用的风速反演模式,已由CMOD4（1997）发展到CMOD5.N（2010）,且该系列模式函数还在不多的发展完善。     

改进的综合BTC颜色矩和DT-CWT的图像检索算法

特征提取是基于内容的图像检索中的关键技术.针对基于单一特征检索效果不理想的问题,提出一种改进的综合颜色和纹理特征的图像检索算法.该算法在YIQ颜色空间中进行特征提取,首先结合方块编码(BTC)的思想,提取颜色矩作为颜色特征;采用双树复小波变换(DT-CWT)提取纹理特征,融合两种特征并利用相似性度量方式进行图像检索.实验结果表明算法所提取的颜色、纹理特征更利于检索,使用综合特征检索的平均查准率比同类算法更高.     

A new SVM-based relevance feedback image retrieval using probabilistic feature and weighted kernel function

Relevance feedback (RF) is an effective approach to bridge the gap between low-level visual features and high-level semantic meanings in content-based image retrieval (CBIR). The support vector machine (SVM) based RF mechanisms have been used in different fields of image retrieval, but they often treat all positive and negative feedback samples equally, which will inevitably degrade the effectiveness of SVM-based RF approaches for CBIR. In fact, positive and negative feedback samples, different positive feedback samples, and different negative feedback samples all always have distinct properties. Moreover, each feedback interaction process is usually tedious and time-consuming because of complex visual features, so if too many times of iteration of feedback are asked, users may be impatient to interact with the CBIR system. To overcome the above limitations, we propose a new SVM-based RF approach using probabilistic feature and weighted kernel function in this paper. Firstly, the probabilistic features of each image are extracted by using principal components analysis (PCA) and the adapted Gaussian mixture models (AGMM) based dimension reduction, and the similarity is computed by employing Kullback–Leibler divergence. Secondly, the positive feedback samples and negative feedback samples are marked, and all feedback samples’ weight values are computed by utilizing the samples-based Relief feature weighting. Finally, the SVM kernel function is modified dynamically according to the feedback samples’ weight values. Extensive simulations on large databases show that the proposed algorithm is significantly more effective than the state-of-the-art approaches.