Automatic localization and annotation of facial features using machine learning techniques

Content-based image retrieval (CBIR) systems traditionally find images within a database that are similar to query image using low level features, such as colour histograms. However, this requires a user to provide an image to the system. It is easier for a user to query the CBIR system using search terms which requires the image content to be described by semantic labels. However, finding a relationship between the image features and semantic labels is a challenging problem to solve. This paper aims to discover semantic labels for facial features for use in a face image retrieval system. Face image retrieval traditionally uses global face-image information to determine similarity between images. However little has been done in the field of face image retrieval to use local face-features and semantic labelling. Our work aims to develop a clustering method for the discovery of semantic labels of face-features. We also present a machine learning based face-feature localization mechanism which we show has promise in providing accurate localization.     

Integrated color,texture and shape information for content-based image retrieval

Feature extraction and the use of the features as query terms are crucial problems in content-based image retrieval (CBIR) systems. The main focus in this paper is on integrated color, texture and shape extraction methods for CBIR. We have developed original CBIR methodology that uses Gabor filtration for determining the number of regions of interest (ROIs), in which fast and effective feature extraction is performed. In the ROIs extracted, texture features based on thresholded Gabor features, color features based on histograms, color moments in YUV space, and shape features based on Zernike moments are then calculated. The features presented proved to be efficient in determining similarity between images. Our system was tested on postage stamp images and Corel photo libraries and can be used in CBIR applications such as postal services.     

Relevance Feedback and Learning in Content-Based Image Search

A major bottleneck in content-based image retrieval (CBIR) systems or search engines is the large gap between low-level image features used to index images and high-level semantic contents of images. One solution to this bottleneck is to apply relevance feedback to refine the query or similarity measures in image search process. In this paper, we first address the key issues involved in relevance feedback of CBIR systems and present a brief overview of a set of commonly used relevance feedback algorithms. Almost all of the previously proposed methods fall well into such framework. We present a framework of relevance feedback and semantic learning in CBIR. In this framework, low-level features and keyword annotations are integrated in image retrieval and in feedback processes to improve the retrieval performance. We have also extended framework to a content-based web image search engine in which hosting web pages are used to collect relevant annotations for images and users' feedback logs are used to refine annotations. A prototype system has developed to evaluate our proposed schemes, and our experimental results indicated that our approach outperforms traditional CBIR system and relevance feedback approaches.     

基于卷积神经网络和流形排序的图像检索算法

针对基于内容的图像检索(CBIR)中低层视觉特征与用户对图像理解的高层语义不一致以及传统的距离度量方式难以真实反映图像之间相似程度等问题,提出了一种基于卷积神经网络(CNN)和流形排序的图像检索算法。首先,将图像输入CNN,通过多层神经网络对图像的监督学习,提取网络中全连接层的图像特征;其次,对图像特征进行归一化处理,然后用高效流形排序(EMR)算法对查询图像所返回的结果进行排序;最后,根据排序的结果返回最相似的图像。在corel数据集上,深度图像特征比基于场景描述的图像特征的平均查准率(mAP)提高了53.74%,流形排序比余弦距离度量方式的mAP提高了18.34%。实验结果表明,所提算法能够有效地提高图像检索的准确率。     

Content-based image retrieval and semantic automatic image annotation based on the weighted average of triangular histograms using support vector machine

In recent years, the rapid growth of multimedia content makes content-based image retrieval (CBIR) a challenging research problem. The content-based attributes of the image are associated with the position of objects and regions within the image. The addition of image content-based attributes to image retrieval enhances its performance. In the last few years, the bag-of-visual-words (BoVW) based image representation model gained attention and significantly improved the efficiency and effectiveness of CBIR. In BoVW-based image representation model, an image is represented as an order-less histogram of visual words by ignoring the spatial attributes. In this paper, we present a novel image representation based on the weighted average of triangular histograms (WATH) of visual words. The proposed approach adds the image spatial contents to the inverted index of the BoVW model, reduces overfitting problem on larger sizes of the dictionary and semantic gap issues between high-level image semantic and low-level image features. The qualitative and quantitative analysis conducted on three image benchmarks demonstrates the effectiveness of the proposed approach based on WATH.     

Collaborative image retrieval via regularized metric learning

In content-based image retrieval (CBIR), relevant images are identified based on their similarities to query images. Most CBIR algorithms are hindered by the semantic gap between the low-level image features used for computing image similarity and the high-level semantic concepts conveyed in images. One way to reduce the semantic gap is to utilize the log data of users' feedback that has been collected by CBIR systems in history, which is also called “collaborative image retrieval.” In this paper, we present a novel metric learning approach, named “regularized metric learning,” for collaborative image retrieval, which learns a distance metric by exploring the correlation between low-level image features and the log data of users' relevance judgments. Compared to the previous research, a regularization mechanism is used in our algorithm to effectively prevent overfitting. Meanwhile, we formulate the proposed learning algorithm into a semidefinite programming problem, which can be solved very efficiently by existing software packages and is scalable to the size of log data. An extensive set of experiments has been conducted to show that the new algorithm can substantially improve the retrieval accuracy of a baseline CBIR system using Euclidean distance metric, even with a modest amount of log data. The experiment also indicates that the new algorithm is more effective and more efficient than two alternative algorithms, which exploit log data for image retrieval.     

Multilabel Neighborhood Propagation for Region-Based Image Retrieval

Content-based image retrieval (CBIR) has been an active research topic in the last decade. As one of the promising approaches, graph-based semi-supervised learning has attracted many researchers. However, while the related work mainly focused on global visual features, little attention has been paid to region-based image retrieval (RBIR). In this paper, a framework based on multilabel neighborhood propagation is proposed for RBIR, which can be characterized by three key properties: 1) For graph construction, in order to determine the edge weights robustly and automatically, mixture distribution is introduced into the earth mover's distance (EMD) and a linear programming framework is involved. 2) Multiple low-level labels for each image can be obtained based on a generative model, and the correlations among different labels are explored when the labels are propagated simultaneously on the weighted graph. 3) By introducing multilayer semantic representation (MSR) and support vector machine (SVM) into the long-term learning, more exact weighted graph for label propagation and more meaningful high-level labels to describe the images can be calculated. Experimental results, including comparisons with the state-of-the-art retrieval systems, demonstrate the effectiveness of our proposal.      

一种基于彩色边缘综合特征的图像检索算法

图像特征的提取与表达是基于内容的图像检索技术基础。边缘是重要的视觉感知信息,也是图像最基本的特征之一,其在图像分析和理解中有重要价值。文中以视觉重要的图像边缘轮廓为基础,提出一种基于彩色边缘综合特征的图像检索算法。该算法首先利用Canny检测算子提取出原始图像的彩色边缘轮廓。然后构造出能全面反映边缘轮廓内容的3种直方图,即加权颜色直方图、角度直方图和梯度方向直方图。最后综合利用上述3种彩色边缘直方图计算图像间的内容相似度,并进行彩色图像检索。仿真实验表明,该算法能够准确和高效地查找出用户所需内容的彩色图像,并且具有较好的查准率和查全率。     

基于模糊支持向量机的面向语义图像检索算法*

为了缩减图像低层特征和高层语义之间的“语义鸿沟”,本文提出一种基于模糊支持向量机的面向语义图像检索(SBIR-FSVM)算法。在提取图像的低层特征的基础上,本文将最小隶属度模糊支持向量机引入到图像检索技术中,获取图像语义信息及消除传统支持向量机(SVM)在多类分类中产生的不可分区域,从而实现面向语义的图像检索。实验结果表明,本文提出的SBIR-FSVM算法与基于SVM的图像检索算法及综合多特征的基于内容的图像检索算法相比均有了显著的改进。     

Content-based facial image retrieval using constrained independent component analysis

Content-based image retrieval (CBIR) is a method of searching, browsing, and querying images according to their content. In this paper, we focus on a specific domain of CBIR that involves the development of a content-based facial image retrieval system based on the constrained independent component analysis (cICA). Originating from independent component analysis (ICA), cICA is a source separation technique that uses priori constraints to extract desired independent components (ICs) from data. By providing query images as the constraints to the cICA, the ICs that share similar probabilistic features with the queries from the database can be extracted. Then, these extracted ICs are used to evaluate the rank of each image according to the query. In our approach, we demonstrate that, in addition to a single image-based query, a compound query with multiple query images can be used to search for images with compounding feature content. The experimental results of our CBIR system tested with different facial databases show that our system can improve retrieval performance by using a compound query. Furthermore, our system allows for online processing without the need to learn query images.     

An image retrieval system with automatic query modification

Most interactive "query-by-example" based image retrieval systems utilize relevance feedback from the user for bridging the gap between the user's implied concept and the low-level image representation in the database. However, traditional relevance feedback usage in the context of content-based image retrieval (CBIR) may not be very efficient due to a significant overhead in database search and image download time in client-server environments. In this paper, we propose a CBIR system that efficiently addresses the inherent subjectivity in user perception during a retrieval session by employing a novel idea of intra-query modification and learning. The proposed system generates an object-level view of the query image using a new color segmentation technique. Color, shape and spatial features of individual segments are used for image representation and retrieval. The proposed system automatically generates a set of modifications by manipulating the features of the query segment(s). An initial estimate of user perception is learned from the user feedback provided on the set of modified images. This largely improves the precision in the first database search itself and alleviates the overheads of database search and image download. Precision-to-recall ratio is improved in further iterations through a new relevance feedback technique that utilizes both positive as well as negative examples. Extensive experiments have been conducted to demonstrate the feasibility and advantages of the proposed system.     

综合颜色纹理形状特征的图像检索

图像特征的提取和使用在基于内容的图像检索中至关重要.研究了在基于内容的图像检索系统中整合颜色,纹理,形状的提取方法.将图像按照一定的规则进行分块,对各个分块分别进行各种特征向量的提取.颜色特征的提取是基于YUV颜色空间的颜色直方图,纹理特征的提取采用Gabor滤波器,形状特征的提取是基于Zernike矩的计算.实验结果表明,综合图像的颜色、形状和纹理特征提高了图像检索的准确性.     

Content-based image retrieval by using tree-structured features and multi-layer self-organizing map

A new approach for content-based image retrieval (CBIR) is described. In this study, a tree-structured image representation together with a multi-layer self-organizing map (MLSOM) is proposed for efficient image retrieval. In the proposed tree-structured image representation, a root node contains the global features, while child nodes contain the local region-based features. This approach hierarchically integrates more information of image contents to achieve better retrieval accuracy compared with global and region features individually. MLSOM in the proposed method provides effective compression and organization of tree-structured image data. This enables the retrieval system to operate at a much faster rate than that of directly comparing query images with all images in databases. The proposed method also adopts a relevance feedback scheme to improve the retrieval accuracy by a respectable level. Our obtained results indicate that the proposed image retrieval system is robust against different types of image alterations. Comparative results corroborate that the proposed CBIR system is promising in terms of accuracy, speed and robustness.     

General methods for L/M-fold resizing of compressed images using lapped transforms

Two general methods are proposed for the arbitrary L/M-fold resizing of compressed images using lapped transforms. These methods provide general resizing frameworks in the transform domain. In order to analyse the image size conversion technique using lapped transforms, the impulse responses and frequency responses for the overall system are derived and analysed. This theoretical analysis shows that the image resizing in the lapped transform domain provides much better characteristics than the conventional image resizing in the discrete cosine transform (DCT) domain. Specifically, by using lapped transforms, the proposed method reduces the blocking effect to a very low level for images which are compressed at low bit rates, since two neighbouring DCT blocks are used in the conversion process, instead of processing each block independently.     

Vector space model adaptation and pseudo relevance feedback for content-based image retrieval

Image retrieval is an important problem for researchers in computer vision and content-based image retrieval (CBIR) fields. Over the last decades, many image retrieval systems were based on image representation as a set of extracted low-level features such as color, texture and shape. Then, systems calculate similarity metrics between features in order to find similar images to a query image. The disadvantage of this approach is that images visually and semantically different may be similar in the low level feature space. So, it is necessary to develop tools to optimize retrieval of information. Integration of vector space models is one solution to improve the performance of image retrieval. In this paper, we present an efficient and effective retrieval framework which includes a vectorization technique combined with a pseudo relevance model. The idea is to transform any similarity matching model (between images) to a vector space model providing a score. A study on several methodologies to obtain the vectorization is presented. Some experiments have been undertaken on Wang, Oxford5k and Inria Holidays datasets to show the performance of our proposed framework.     

一种结合多示例学习的图像检索方法①

提出一种基于多示例学习（Multiple—instance learning）的图像检索方法,将多示例学习应用于图像检索中,以有效的处理图像的歧义性。该方法首先将图像作为多示例包,其次采用自适应k—means图像分割算法将图像自动分成多个示例,然后根据用户选择的实例图像生成正包和反包,再采用EM—DD（expectation maximization diversedensity）算法进行多示例学习,实现图像检索和相关反馈,最终使用户得到比较满意的结果。     

Complementary relevance feedback-based content-based image retrieval

We propose a complementary relevance feedback-based content-based image retrieval (CBIR) system. This system exploits the synergism between short-term and long-term learning techniques to improve the retrieval performance. Specifically, we construct an adaptive semantic repository in long-term learning to store retrieval patterns of historical query sessions. We then extract high-level semantic features from the semantic repository and seamlessly integrate low-level visual features and high-level semantic features in short-term learning to effectively represent the query in a single retrieval session. The high-level semantic features are dynamically updated based on users’ query concept and therefore represent the image’s semantic concept more accurately. Our extensive experimental results demonstrate that the proposed system outperforms its seven state-of-the-art peer systems in terms of retrieval precision and storage space on a large scale imagery database.     

Region-based Image Retrieval Using Probabilistic Feature Relevance Learning

Region-Based Image Retrieval (RBIR), a specialisation of content-based image retrieval, is a promising and important research area. RBIR usually requires good segmentation, which is often difficult to achieve in practice for several reasons, such as varying environmental conditions and occlusion. It is, therefore, imperative to develop effective mechanisms for interactive, region-based visual query in order to provide confident retrieval performance. In this paper, we present a novel RBIR system, Finding Region In the Pictures (FRIP), that uses human-centric relevance feedback to create similarity metric on-the-fly in order to overcome some of the limitations associated with RBIR systems. We use features such as colour, texture, normalised area, shape and location, extracted from each region of a segmented image, to represent image content. For each given query, we estimate local feature relevance using probabilistic relevance model, from which to create a flexible metric that is highly adaptive to query location. As a result, local data densities can be sufficiently exploited, whereby rapid performance improvement can be achieved. The efficacy of our method is validated and compared against other competing techniques using real world image data.