Fuzzy color histogram and its use in color image retrieval

A conventional color histogram (CCH) considers neither the color similarity across different bins nor the color dissimilarity in the same bin. Therefore, it is sensitive to noisy interference such as illumination changes and quantization errors. Furthermore, CCHs large dimension or histogram bins requires large computation on histogram comparison. To address these concerns, this paper presents a new color histogram representation, called fuzzy color histogram (FCH), by considering the color similarity of each pixel's color associated to all the histogram bins through fuzzy-set membership function. A novel and fast approach for computing the membership values based on fuzzy c-means algorithm is introduced. The proposed FCH is further exploited in the application of image indexing and retrieval. Experimental results clearly show that FCH yields better retrieval results than CCH. Such computing methodology is fairly desirable for image retrieval over large image databases.     

基于分块颜色直方图和GWLBP的图像检索算法

为了提高多特征融合图像检索的效果,本文提出了一种基于分块颜色直方图和GWLBP的图像检索算法。算法采用K-means均值聚类对RGB颜色空间进行颜色聚类,再将4×4均匀分块图像分成9个子块,提取每个子块的颜色体积直方图,并赋予不同权值计算颜色特征;利用Gabor滤波器组对输入图像进行不同分辨率和方向滤波,然后将不同方向上局部滤波器输出结果与全局滤波器输出结果的平均值进行比较,并进行二值化,据此提出3种不同的GWLBP算子来提取纹理特征。最后对图像的颜色和纹理特征高斯归一化,采用加权平均来融合颜色和纹理的特征距离。通过实验仿真可知,与其他3种算法相比,本算法对正常和有旋转倾向的图像都有较高的查全率和查准率。     

Directional detail histogram for image retrieval

The authors propose a new indexing technique, for image retrieval, which calculates the histogram of the directional detail content in a given image. A multiresolution analysis is applied to extract directional information which is then mapped into three-dimensional vectors and presented as a histogram. This allows the use of histogram query techniques to be readily applied for retrieval     

基于颜色信息的视频检索系统的实现

针对某电视台在转播节目时需要自动切换图像内容的要求，设计并实现了一套视频检索系统。这套系统充分利用彩色图像的颜色信息和颜色直方图的唯一性，通过计算两帧图像之间简化的直方图距离来判断它们是否相似。从实验结果看，这套系统能够快速，准确地从序列中检索出“感兴趣”的图像，并且自动切换成自己的视频内容，在“感兴趣”序列结束时也能快速地给出相应判断，整个系统基本满足应用的要求。     

Robust non-blind color video watermarking using QR decomposition and entropy analysis

Issues such as content identification, document and image security, audience measurement, ownership and copyright among others can be settled by the use of digital watermarking. Many recent video watermarking methods show drops in visual quality of the sequences. The present work addresses the aforementioned issue by introducing a robust and imperceptible non-blind color video frame watermarking algorithm. The method divides frames into moving and non-moving parts. The non-moving part of each color channel is processed separately using a block-based watermarking scheme. Blocks with an entropy lower than the average entropy of all blocks are subject to a further process for embedding the watermark image. Finally a watermarked frame is generated by adding moving parts to it. Several signal processing attacks are applied to each watermarked frame in order to perform experiments and are compared with some recent algorithms. Experimental results show that the proposed scheme is imperceptible and robust against common signal processing attacks.     

用于纹理检索的多尺度复杂性纹理描述子

提出了基于复杂性度量和图像多尺度分析的纹理特征提取方法,提取后的特征用于纹理检索.使用的复杂性度量为一维和二维CO复杂性.采用的图像多尺度分析方法有金字塔分解、小波分解(Haar小波与Db2小波)和二维经验模式分解.实验结果表明:提出的基于Db2小波分解的一维和二维CO复杂性特征是适于纹理检索的特征.它们均取得了和Ga...     

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。     

Models for motion-based video indexing and retrieval

With the rapid proliferation of multimedia applications that require video data management, it is becoming more desirable to provide proper video data indexing techniques capable of representing the rich semantics in video data. In real-time applications, the need for efficient query processing is another reason for the use of such techniques. We present models that use the object motion information in order to characterize the events to allow subsequent retrieval. Algorithms for different spatiotemporal search cases in terms of spatial and temporal translation and scale invariance have been developed using various signal and image processing techniques. We have developed a prototype video search engine, PICTURESQUE (pictorial information and content transformation unified retrieval engine for spatiotemporal queries) to verify the proposed methods. Development of such technology will enable true multimedia search engines that will enable indexing and searching of the digital video data based on its true content.     

视频检索中图像信息量度量

综合考虑信息量度量的速度、性能要求,提出了相适应的显著图、多特征融合模型;基于区域划分融入空间关系,提出了分块信息熵的图像信息量度量方法（SEII）;构建了信息量度量的标注数据集,并设计了性能验证方法。实验结果表明该度量方法符合人眼视觉的评价结果。度量方法在实际视频检索系统中进行对比应用测试,测试表明mAP提高4.4%,检索速度提高1.5倍。     

融合颜色直方图响应的时空上下文跟踪算法

时空上下文(STC)跟踪算法在特征表达、尺度自适应策略等方面存在缺陷,当出现目标突然形变、局部遮挡或尺度变化等情况时,跟踪器的性能会严重退化。通过对STC算法进行改进,提出了一种融合颜色直方图响应的时空上下文跟踪算法。基于颜色统计的模型对运动模糊和目标形变等影响因素不敏感,和时空上下文模型具有良好的互补性质,在响应层融合后能够提升算法的鲁棒性。此外,采用基于多尺度金字塔模型的尺度搜索策略替换STC算法中原有的尺度估计策略,进行更精准的自适应尺度估计。在大规模公开数据集上的测试结果表明,本文算法在不同影响因素的复杂环境下展现了更为良好的跟踪性能和适应性,并且平均跟踪速度达到134.2帧/秒。     

SIFT-flow-based color correction for multi-view video

During the multi-view video acquisition, color variation across the views tends to be incurred due to different camera positions, orientations, and local lighting conditions. Such color variation will inevitably deteriorate the performance of the follow-up multi-view video processing, such as multi-view video coding (MVC). To address this problem, an effective color correction algorithm, called the SIFT flow-based color correction (SFCC), is proposed in this paper. First, the SIFT-flow technique is used to establish point-to-point correspondences across all the views of the multi-view video. The average color is then computed based on those identified common corresponding points and used as the reference color. By minimizing the energy of the difference yielded between the color of those identified common corresponding points in each view with respect to the reference color, the color correction matrix for each view can be obtained and used to correct its color. Experimental results have shown that the proposed SFCC algorithm is able to effectively eliminate the color variation inherited in multi-view video. By further exploiting the developed SFCC algorithm as a pre-processing for the MVC, extensive simulation results have shown that the coding efficiency of the color-corrected multi-view video can be greatly improved (on average, 0.85 dB, 1.27 dB and 1.63 dB gain for Y, U, and V components, respectively), compared with that of the original multi-view video without color correction.     

Content based video indexing and retrieval

Video management tools and techniques are based on pixels rather than perceived content. Thus, state-of-the-art video editing systems can easily manipulate such things as time codes and image frames, but they cannot “know,” for example, what a basketball is. Our research addresses four areas of content-based video management     

Learning semantic and visual similarity for endomicroscopy video retrieval

Content-based image retrieval (CBIR) is a valuable computer vision technique which is increasingly being applied in the medical community for diagnosis support. However, traditional CBIR systems only deliver visual outputs, i.e., images having a similar appearance to the query, which is not directly interpretable by the physicians. Our objective is to provide a system for endomicroscopy video retrieval which delivers both visual and semantic outputs that are consistent with each other. In a previous study, we developed an adapted bag-of-visual-words method for endomicroscopy retrieval, called "Dense-Sift," that computes a visual signature for each video. In this paper, we present a novel approach to complement visual similarity learning with semantic knowledge extraction, in the field of in vivo endomicroscopy. We first leverage a semantic ground truth based on eight binary concepts, in order to transform these visual signatures into semantic signatures that reflect how much the presence of each semantic concept is expressed by the visual words describing the videos. Using cross-validation, we demonstrate that, in terms of semantic detection, our intuitive Fisher-based method transforming visual-word histograms into semantic estimations outperforms support vector machine (SVM) methods with statistical significance. In a second step, we propose to improve retrieval relevance by learning an adjusted similarity distance from a perceived similarity ground truth. As a result, our distance learning method allows to statistically improve the correlation with the perceived similarity. We also demonstrate that, in terms of perceived similarity, the recall performance of the semantic signatures is close to that of visual signatures and significantly better than those of several state-of-the-art CBIR methods. The semantic signatures are thus able to communicate high-level medical knowledge while being consistent with the low-level visual signatures and much shorter than them. In our resulting retrieval system, we decide to use visual signatures for perceived similarity learning and retrieval, and semantic signatures for the output of an additional information, expressed in the endoscopist own language, which provides a relevant semantic translation of the visual retrieval outputs.