基于图像内容的非对称数字水印

在小波变换的基础上,提出了一种基于图像内容的非对称数字水印。首先,利用SOBEL算子提取经过小波分解后的低频区域中的图像的边缘特征的系数,存储在一向量中,利用非线性函数对水印加密,并将水印嵌入图像的边缘系数中。实验结果表明该算法具有极强的鲁棒性和不可见性。在实验过程中,利用MATLAB的引擎,在VC++中实现调用MATLAB的工具箱,快捷的编写了简洁的原代码。

Auto-expanded multi query examples technology in content-based image retrieval

In order to narrow the semantic gap existing in content-based image retrieval （CBIR）,a novel retrieval technology called auto-extended multi query examples （AMQE） is proposed.It expands the single one query image used in traditional image retrieval into multi query examples so as to include more image features related with semantics.Retrieving images for each of the multi query examples and integrating the retrieval results,more relevant images can be obtained.The property of the recall-precision curve of a general retrieval algorithm and the K-means clustering method are used to realize the expansion according to the distance of image features of the initially retrieved images.The experimental results demonstrate that the AMQE technology can greatly improve the recall and precision of the original algorithms.

Active learning based on maximizing information gain for content-based image retrieval

This paper describes a new method for active learning in content-based image retrieval. The proposed method firstly uses support vector machine (SVM) classifiers to learn an initial query concept. Then the proposed active learning scheme employs similarity measure to check the current version space and selects images with maximum expected information gain to solicit user's label. Finally, the learned query is refined based on the user's further feedback. With the combination of SVM classifier and similarity measure, the proposed method can alleviate model bias existing in each of them. Our experiments on several query concepts show that the proposed method can learn the user's query concept quickly and effectively only with several iterations.

Design and Implementation of Patient Online Guide System

The design and implementation of an online guide system were presented for patients.The system offers a website that makes it easier for patients,or any ordinary visitor to diagnose their disease by simply uploading their MRI image,the website also assists the patient in determining the doctor they need to see,and finding the nearest hospital or blood bank.To build the website,SQL Server,MAT-LAB,and Adobe Dreamweaver were utilized.The rapid increase in digital images produced in hospitals us-ing medical imaging techniques such as X-Ray,CT scans,MRI,and ultrasound has increased the demand for efficient image retrieval systems.Personal description,and annotation of each image in a large database using text-based indexing(also known as the Metadata approach)is time-consuming,and impractical,making Content-based Image Retrieval(CBIR)a better option.CBIR system proposed returns results based on visual features of the image,such as color,texture,and shape.

Fast uniform content-based satellite image registration using the scale-invariant feature transform descriptor

Content-based satellite image registration is a difficult issue in the fields of remote sensing and image processing. The difficulty is more significant in the case of matching multisource remote sensing images which suffer from illumination, rotation, and source differences. The scale-invariant feature transform （SIFT） algorithm has been used successfully in satellite image registration problems. Also, many researchers have applied a local SIFT descriptor to improve the image retrieval process. Despite its robustness, this algorithm has some difficulties with the quality and quantity of the extracted local feature points in multisource remote sensing. Furthermore, high dimensionality of the local features extracted by SIFT results in time-consuming computational processes alongside high storage requirements for saving the relevant information, which are important factors in content-based image retrieval （CBIR） applications. In this paper, a novel method is introduced to transform the local SIFT features to global features for multisource remote sensing. The quality and quantity of SIFT local features have been enhanced by applying contrast equalization on images in a pre-processing stage. Considering the local features of each image in the reference database as a separate class, linear discriminant analysis （LDA） is used to transform the local features to global features while reducing di- mensionality of the feature space. This will also significantly reduce the computational time and storage required. Applying the trained kernel on verification data and mapping them showed a successful retrieval rate of 91.67% for test feature points.