Embedding neural networks for semantic association in content based image retrieval

Content based image retrieval (CBIR) systems provide potential solution of retrieving semantically similar images from large image repositories against any query image. The research community are competing for more effective ways of content based image retrieval, so they can be used in serving time critical applications in scientific and industrial domains. In this paper a Neural Network based architecture for content based image retrieval is presented. To enhance the capabilities of proposed work, an efficient feature extraction method is presented which is based on the concept of in-depth texture analysis. For this wavelet packets and Eigen values of Gabor filters are used for image representation purposes. To ensure semantically correct image retrieval, a partial supervised learning scheme is introduced which is based on K-nearest neighbors of a query image, and ensures the retrieval of images in a robust way. To elaborate the effectiveness of the presented work, the proposed method is compared with several existing CBIR systems, and it is proved that the proposed method has performed better then all of the comparative systems.

     

Automatic content-based retrieval and semantic classification of video content

The problem of video classification can be viewed as discovering the signature patterns in the elemental features of a video class. In order to solve this problem, a large and diverse set of video features is proposed in this paper. The contributions of the paper further lie in dealing with high-dimensionality induced by the feature space and in presenting an algorithm based on two-phase grid searching for automatic parameter selection for support vector machine (SVM). The framework thus is directed to bridge the gap between low-level features and semantic video classes. The experimental results and comparison with state-of-the-art learning tools on more than 5000 video segments show the effectiveness of our approach.     

A semantic modeling approach for image retrieval by content

We introduce a semantic data model to capture the hierarchical, spatial, temporal, and evolutionary semantics of images in pictorial databases. This model mimics the user's conceptual view of the image content, providing the framework and guidelines for preprocessing to extract image features. Based on the model constructs, a spatial evolutionary query language (SEQL), which provides direct image object manipulation capabilities, is presented. With semantic information captured in the model, spatial evolutionary queries are answered efficiently. Using an object-oriented platform, a prototype medical-image management system was implemented at UCLA to demonstrate the feasibility of the proposed approach.     

Feed-forward content based image retrieval using adaptive tetrolet transforms

This paper proposes a new approach for content based image retrieval based on feed-forward architecture and Tetrolet transforms. The proposed method addresses the problems of accuracy and retrieval time of the retrieval system. The proposed retrieval system works in two phases: feature extraction and retrieval. The feature extraction phase extracts the texture, edge and color features in a sequence. The texture features are extracted using Tetrolet transform. This transform provides better texture analysis by considering the local geometry of the image. Edge orientation histogram is used for retrieving the edge feature while color histogram is used for extracting the color features. Further retrieval phase retrieves the images in the feed-forward manner. At each stage, the number of images for next stage is reduced by filtering out irrelevant images. The Euclidean distance is used to measure the distance between the query and database images at each stage. The experimental results on COREL- 1 K and CIFAR - 10 benchmark databases show that the proposed system performs better in terms of the accuracy and retrieval time in comparison to the state-of-the-art methods.     

基于语义分类的外观专利图像快速检索系统

针对大规模专利图像特征库的特点,使用边缘轮廓距离与分块特征相结合的方法提取低层视觉特征,结合基于K均值聚类的分类索引方法,兼顾语义相似和视觉特征相似,对专利图像库数据构建索引结构,实现了先分类后检索的功能。实验结果表明,方法不仅提高了检索速度,而且提高了检索的语义敏感度。     

Edge based features for content based image retrieval

The common problem in content based image retrieval (CBIR) is selection of features. Image characterization with lesser number of features involving lower computational cost is always desirable. Edge is a strong feature for characterizing an image. This paper presents a robust technique for extracting edge map of an image which is followed by computation of global feature (like fuzzy compactness) using gray level as well as shape information of the edge map. Unlike other existing techniques it does not require pre segmentation for the computation of features. This algorithm is also computationally attractive as it computes different features with limited number of selected pixels.     

场景语义树图像标注方法

自动图像标注是一项具有挑战性的工作,它对于图像分析理解和图像检索都有着重要的意义.在自动图像标注领域,通过对已标注图像集的学习,建立语义概念空间与视觉特征空间之间的关系模型,并用这个模型对未标注的图像集进行标注.由于低高级语义之间错综复杂的对应关系,使目前自动图像标注的精度仍然较低.而在场景约束条件下可以简化标注与视觉特征之间的映射关系,提高自动标注的可靠性.因此提出一种基于场景语义树的图像标注方法.首先对用于学习的标注图像进行自动的语义场景聚类,对每个场景语义类别生成视觉场景空间,然后对每个场景空间建立相应的语义树.对待标注图像,确定其语义类别后,通过相应的场景语义树,获得图像的最终标注.在Corel5K图像集上,获得了优于TM(translation model)、CMRM(cross media relevance model)、CRM(continous-space relevance model)、PLSA-GMM(概率潜在语义分析-高期混合模型)等模型的标注结果.     

基于隐含语义分析的音乐检索

为提高音乐检索效率,使检索结果与搜索目的更接近,提出了基于隐含语义分析的音乐检索方法.将曲谱表示为标准音符和音转的交替串,基于每个交替串使用频率高于包含它的多交替串排列的事实,设计了音乐词汇统计算法.为使各分句能整齐地转化为相同维数的向量,使用最长的分句长度作为标准维数,基于增加频率和的原则进行单词的重新分割.实验结果表明,基于隐含语义分析的检索能获得令人满意的检索结果.     

Automatic image annotation and retrieval using group sparsity

Automatically assigning relevant text keywords to images is an important problem. Many algorithms have been proposed in the past decade and achieved good performance. Efforts have focused upon model representations of keywords, whereas properties of features have not been well investigated. In most cases, a group of features is preselected, yet important feature properties are not well used to select features. In this paper, we introduce a regularization-based feature selection algorithm to leverage both the sparsity and clustering properties of features, and incorporate it into the image annotation task. Using this group-sparsity-based method, the whole group of features [e.g., red green blue (RGB) or hue, saturation, and value (HSV)] is either selected or removed. Thus, we do not need to extract this group of features when new data comes. A novel approach is also proposed to iteratively obtain similar and dissimilar pairs from both the keyword similarity and the relevance feedback. Thus, keyword similarity is modeled in the annotation framework. We also show that our framework can be employed in image retrieval tasks by selecting different image pairs. Extensive experiments are designed to compare the performance between features, feature combinations, and regularization-based feature selection methods applied on the image annotation task, which gives insight into the properties of features in the image annotation task. The experimental results demonstrate that the group-sparsity-based method is more accurate and stable than others.     

综合纹理特征和语义信息的医学图像检索

提出一种结合图像分块纹理特征和语义信息的医学胸片图像检索方法。同时,介绍了颜色特征提取方法中的颜色相关图算法。据此,实现了一个图像检索原型系统,依据所设计的评价实验,将不同实验的检索结果进行了比较和分析。实验证明,结合图像分块纹理特征和语义信息的检索方法具有较好的检索效果。     

面向特定目标自识别的交通图像语义检索方法

为了从海量的道路交通图像中检索出违反交通法规的图像,提出了一种特定目标自识别的语义图像检索方法。首先,通过交通领域专家建立交通领域本体及道路交通规则描述;然后,通过卷积神经网络（CNN）对交通图像的特征进行提取,并结合改进的支持向量机决策树（SVM-DT）算法对图像特征进行分类的策略,对交通图像中的特定目标及目标间空间位置关系进行自动识别,并映射成为相应的本体实例及其对象之间的关联关系（规则实例）;最后,利用本体实例和规则实例,通过推理得到语义检索结果。实验结果表明,相比关键字和本体交通图像语义检索方法,所提方法具有更高的准确率、召回率和检索效率。     

基于内容和语义的三维模型检索综述

多媒体信息的检索是信息复用的重要途径。三维模型检索作为三维建模过程中的关键技术之一,近年来随着三维建模的广泛运用而被深入研究。针对目前三维模型检索技术的进展,首先介绍了基于内容的检索技术,按照提取的特征将其分为四类：基于统计数据、基于几何外形、基于拓扑结构和基于视觉特征,分别介绍各类技术的主要成果和优缺点;然后介绍考虑语义信息,解决“语义鸿沟”现象的基于语义的检索方法,根据切入角度将其分为三类：相关性反馈、主动学习和本体技术,随后介绍了各类技术的相互关系与特点;最后总结和提出了三维模型检索的未来研究的发展方向。     

An efficient bi-layer content based image retrieval system

Multimedia Tools and Applications - Large amount of multi-media content, generated by various image capturing devices, is shared and downloaded by millions of users across the globe, every second....     

隐私保护的图像内容检索技术研究综述

随着智能设备与社交媒体的广泛普及,图片数据的数量急剧增长,数据拥有者将本地数据外包至云平台,在云服务器上实现数据的存储、分享和检索。然而,图像数据含有大量有关用户的敏感信息,外部攻击者和不完全可信的云服务器都试图获取原始图像的内容,窥探用户隐私,造成严重的隐私泄露风险。回顾了近年来隐私保护需求下图像内容检索技术的研究进展,总结了应用于该技术的图像加密算法,包括同态加密、随机化加密和比较加密,围绕这3种密码技术,详细分析和比较了典型的解决方案,并介绍了索引构造的改进策略。最后,总结和展望了未来的研究趋势。     

A content based image information retrieval and video thumbnail extraction framework using SOM

Searching an image or a video in a huge volume of graphical data is a tedious time-consuming process. If this search is performed using the conventional element matching technique, the complexity of the search will render the system useless. To overcome this problem, the current paper proposes a Content-Based Image Retrieval (CBIR) and a Content-Based Video Retrieval (CBVR) technique using clustering algorithms based on neural networks. Neural networks have proved to be quite powerful for dimensionality reduction due to their parallel computations. Retrieval of images in a large database on the basis of the content of the query image has been proved fast and efficient through practical results. Two images of the same object, but taken from different camera angles or have rotational and scaling transforms is also matched effectively. In medical domain, CBIR has proved to be a boon to the doctors. The tumor, cancer etc can be easily deducted comparing the images with normal to the images with diseases. Java and Weka have been used for implementation. The thumbnails extracted from the video facilitates the video search in a large videos database. The unsupervised nature of Self Organizing Maps (SOM) has made the software all the more robust.

     

基于内容图像检索的聚类算法研究*

介绍了基于内容图像检索的系统结构、特征提取等内容,并将数据挖掘的聚类算法与之结合,对各种聚类算法进行了总结,最后提出了一些未来的发展方向。     

Automatic image annotation and retrieval using weighted feature selection

The development of technology generates huge amounts of non-textual information, such as images. An efficient image annotation and retrieval system is highly desired. Clustering algorithms make it possible to represent visual features of images with finite symbols. Based on this, many statistical models, which analyze correspondence between visual features and words and discover hidden semantics, have been published. These models improve the annotation and retrieval of large image databases. However, image data usually have a large number of dimensions. Traditional clustering algorithms assign equal weights to these dimensions, and become confounded in the process of dealing with these dimensions. In this paper, we propose weighted feature selection algorithm as a solution to this problem. For a given cluster, we determine relevant features based on histogram analysis and assign greater weight to relevant features as compared to less relevant features. We have implemented various different models to link visual tokens with keywords based on the clustering results of K-means algorithm with weighted feature selection and without feature selection, and evaluated performance using precision, recall and correspondence accuracy using benchmark dataset. The results show that weighted feature selection is better than traditional ones for automatic image annotation and retrieval.