Deep linear discriminant analysis hashing for image retrieval

Multimedia Tools and Applications - Currently, due to the exponential growth of online images, it is necessary to consider image retrieval among large number of images, which is very time-consuming...     

Generalized biased discriminant analysis for content-based image retrieval

Biased discriminant analysis (BDA) is one of the most promising relevance feedback (RF) approaches to deal with the feedback sample imbalance problem for content-based image retrieval (CBIR). However, the singular problem of the positive within-class scatter and the Gaussian distribution assumption for positive samples are two main obstacles impeding the performance of BDA RF for CBIR. To avoid both of these intrinsic problems in BDA, in this paper, we propose a novel algorithm called generalized BDA (GBDA) for CBIR. The GBDA algorithm avoids the singular problem by adopting the differential scatter discriminant criterion (DSDC) and handles the Gaussian distribution assumption by redesigning the between-class scatter with a nearest neighbor approach. To alleviate the overfitting problem, GBDA integrates the locality preserving principle; therefore, a smooth and locally consistent transform can also be learned. Extensive experiments show that GBDA can substantially outperform the original BDA, its variations, and related support-vector-machine-based RF algorithms.     

Using discriminant eigenfeatures for image retrieval

This paper describes the automatic selection of features from an image training set using the theories of multidimensional discriminant analysis and the associated optimal linear projection. We demonstrate the effectiveness of these most discriminating features for view-based class retrieval from a large database of widely varying real-world objects presented as “well-framed” views, and compare it with that of the principal component analysis     

基于Log-Gabor小波和二维半监督判别分析的人脸图像检索*

针对光照、表情变化给人脸识别造成的影响以及大型人脸图像库的训练样本中只有部分标记的问题,结合多通道Log-Gabor小波和半监督流形学习算法,提出一种新的人脸图像检索方法。该方法首先使用Log-Ga-bor小波对人脸图像进行滤波获得特征矩阵,进一步利用提出的二维半监督流形学习算法进行维数约简,得到低维判别特征。由于该方法直接作用于Log-Gabor特征矩阵,克服了小样本带来的奇异问题;另外,通过充分利用标记和未标记信息,还保留了数据的局部流形结构,增强了特征匹配的相似性。在CMU PIE和AR人脸数据库上的实验结果表明,该方法有效且优于其他方法。     

结合判别相关分析与特征融合的遥感图像检索

目的 高分辨率遥感图像检索中,单一特征难以准确描述遥感图像的复杂信息。为了充分利用不同卷积神经网络（convolutional neural networks,CNN）的学习参数来提高遥感图像的特征表达,提出一种基于判别相关分析的方法融合不同CNN的高层特征。方法 将高层特征作为特殊的卷积层特征处理,为了更好地保留图像的原始空间信息,在图像的原始输入尺寸下提取不同高层特征,再对高层特征进行最大池化来获得显著特征;计算高层特征的类间散布矩阵,结合判别相关分析来增强同类特征的联系,并突出不同类特征之间的差异,从而提高特征的判别力;选择串联与相加两种方法来对不同特征进行融合,用所得融合特征来检索高分辨率遥感图像。结果 在UC-Merced、RSSCN7和WHU-RS19数据集上的实验表明,与单一高层特征相比,绝大多数融合特征的检索准确率和检索时间都得到有效改进。其中,在3个数据集上的平均精确率均值（mean average precision,mAP）分别提高了10.4% 14.1%、5.7% 9.9%和5.9% 17.6%。以检索能力接近的特征进行融合时,性能提升更明显。在UC-Merced数据集上,融合特征的平均归一化修改检索等级（average normalized modified retrieval rank,ANMRR）和mAP达到13.21%和84.06%,与几种较新的遥感图像检索方法相比有一定优势。结论 本文提出的基于判别相关分析的特征融合方法有效结合了不同CNN高层特征的显著信息,在降低特征冗余性的同时,提升了特征的表达能力,从而提高了遥感图像的检索性能。     

Hierarchical deep semantic hashing for fast image retrieval

Multimedia Tools and Applications - Content-Based large-scale image retrieval has recently attracted considerable attention because of the explosive increase of online images. Inspired by recent...     

Direct kernel biased discriminant analysis: a new content-based image retrieval relevance feedback algorithm

In recent years, a variety of relevance feedback (RF) schemes have been developed to improve the performance of content-based image retrieval (CBIR). Given user feedback information, the key to a RF scheme is how to select a subset of image features to construct a suitable dissimilarity measure. Among various RF schemes, biased discriminant analysis (BDA) based RF is one of the most promising. It is based on the observation that all positive samples are alike, while in general each negative sample is negative in its own way. However, to use BDA, the small sample size (SSS) problem is a big challenge, as users tend to give a small number of feedback samples. To explore solutions to this issue, this paper proposes a direct kernel BDA (DKBDA), which is less sensitive to SSS. An incremental DKBDA (IDKBDA) is also developed to speed up the analysis. Experimental results are reported on a real-world image collection to demonstrate that the proposed methods outperform the traditional kernel BDA (KBDA) and the support vector machine (SVM) based RF algorithms.     

Cross-media retrieval based on linear discriminant analysis

Multimedia Tools and Applications - Existing cross-media retrieval approaches usually project low-level features from different modalities of data into a common subspace, in which the similarity of...     

Hierarchical multiple-SIMD architecture for image analysis

Real-time image analysis requires the use of massively parallel machines. Conventional parallel machines consist of an array of identical processors organized in either single instruction multiple data (SIMD) or multiple instruction multiple data (MIMD) configurations. Machines of this type generally only operate effectively on parts of the image analysis problem. SIMD on the low level processing and MIMD on the high level processing. In this paper we describe the Warwick Pyramid Machine, an architecture consisting of both SIMD and MIMD parts in a multiple-SIMD (MSIMD) organization which can operate effectively at all levels of the image analysis problem.     

Tensor-driven low-rank discriminant analysis for image set classification

Multimedia Tools and Applications - Classification based on image sets has recently attracted great interest in computer vision community. In this paper, we proposed a transductive Tensor-driven...     

Hierarchical discriminant regression

The main motivation of this paper is to propose a classification and regression method for challenging high-dimensional data. The proposed technique casts classification problems and regression problems into a unified regression problem. This unified view enables classification problems to use numeric information in the output space that is available for regression problems but are traditionally not readily available for classification problems. A doubly clustered subspace-based hierarchical discriminating regression (HDR) method is proposed. The major characteristics include: (1) Clustering is performed in both output space and input space at each internal node, termed "doubly clustered." Clustering in the output space provides virtual labels for computing clusters in the input space. (2) Discriminants in the input space are automatically derived from the clusters in the input space. (3) A hierarchical probability distribution model is applied to the resulting discriminating subspace at each internal node. This realizes a coarse-to-fine approximation of probability distribution of the input samples, in the hierarchical discriminating subspaces. (4) To relax the per class sample requirement of traditional discriminant analysis techniques, a sample-size dependent negative-log-likelihood (NLL) is introduced. This new technique is designed for automatically dealing with small-sample applications, large-sample applications, and unbalanced-sample applications. (5) The execution of the HDR method is fast, due to the empirical logarithmic time complexity of the HDR algorithm. Although the method is applicable to any data, we report the experimental results for three types of data: synthetic data for examining the near-optimal performance, large raw face-image databases, and traditional databases with manually selected features along with a comparison with some major existing methods.     

Subclass discriminant Nonnegative Matrix Factorization for facial image analysis

Nonnegative Matrix Factorization (NMF) is among the most popular subspace methods, widely used in a variety of image processing problems. Recently, a discriminant NMF method that incorporates Linear Discriminant Analysis inspired criteria has been proposed, which achieves an efficient decomposition of the provided data to its discriminant parts, thus enhancing classification performance. However, this approach possesses certain limitations, since it assumes that the underlying data distribution is unimodal, which is often unrealistic. To remedy this limitation, we regard that data inside each class have a multimodal distribution, thus forming clusters and use criteria inspired by Clustering based Discriminant Analysis. The proposed method incorporates appropriate discriminant constraints in the NMF decomposition cost function in order to address the problem of finding discriminant projections that enhance class separability in the reduced dimensional projection space, while taking into account subclass information. The developed algorithm has been applied for both facial expression and face recognition on three popular databases. Experimental results verified that it successfully identified discriminant facial parts, thus enhancing recognition performance.     

A multi-manifold discriminant analysis method for image feature extraction

In this paper, we propose a Multi-Manifold Discriminant Analysis (MMDA) method for an image feature extraction and pattern recognition based on graph embedded learning and under the Fisher discriminant analysis framework. In an MMDA, the within-class graph and between-class graph are, respectively, designed to characterize the within-class compactness and the between-class separability, seeking for the discriminant matrix to simultaneously maximize the between-class scatter and minimize the within-class scatter. In addition, in an MMDA, the within-class graph can represent the sub-manifold information, while the between-class graph can represent the multi-manifold information. The proposed MMDA is extensively examined by using the FERET, AR and ORL face databases, and the PolyU finger-knuckle-print databases. The experimental results demonstrate that an MMDA is effective in feature extraction, leading to promising image recognition performance.     

Hierarchical BoW with segmental sparse coding for large scale image classification and retrieval

Multimedia Tools and Applications - The bag-of-words (BoW) has been widely regarded as the most successful algorithms for content-based image related tasks, such as large scale image retrieval,...     

基于颜色特征相似度判别的肿瘤图像检索研究

给出医学图像检索的工作流程,分析并选择颜色特征作为医学图像检索的重要依据。经过颜色空间选择、颜色空间转换、颜色量化和颜色特征提取完成医学图像特征抽取,设计了基于欧式距离、加权欧式距离和直方图相交的相似度判别算法。实验结果表明,检索单幅图像的处理时间不超过0.432s;从平均值角度考虑,正常图像的召回率优于其它类别图像;正常图像检索的准确率高于癌变图像检索的准确率。     

基于核线性分类分析的三维模型检索算法

为提高检索精确度,提出了一种利用核线性分类分析来对模型特征进行优化的新方法。其主要思想是通过满足Mercer条件的非线性映射将低维空间下线性不可分的样本映射到高维空间,在高维空间中利用线性分类分析将原有的三维模型特征投影到特定的子空间。该方法能够在保持类间距离基础上得到具有鉴别信息的低维特征用于三维模型检索。实验结果表明,核线性分类分析方法速度较快,可在秒级完成三维特征优化,同时优化特征在本文测试数据集上可平均提高搜索准确度15%。     

Hierarchical image analysis using irregular tessellations

A novel multiresolution image analysis technique based on hierarchies of irregular tessellations generated in parallel by independent stochastic processes is presented. Like traditional image pyramids these hierarchies are constructed in a number of steps on the order of log(image-size) steps. However, the structure of a hierarchy is adapted to the image content and artifacts of rigid resolution reduction are avoided. Two applications of these techniques are presented: connected component analysis of labeled images and segmentation of gray level images. In labeled images, every connected component is reduced to a separate root, with the adjacency relations among the components also extracted. In gray level images the output is a segmentation of the image into a small number of classes as well as the adjacency graph of the classes     

Statistical correlation analysis in image retrieval

A statistical correlation model for image retrieval is proposed. This model captures the semantic relationships among images in a database from simple statistics of user-provided relevance feedback information. It is applied in the post-processing of image retrieval results such that more semantically related images are returned to the user. The algorithm is easy to implement and can be efficiently integrated into an image retrieval system to help improve the retrieval performance. Preliminary experimental results on a database of 100,000 images show that the proposed model could improve image retrieval performance for both content- and text-based queries.     

Medical image retrieval based on complexity analysis

Texture is one of the most important visual attributes used in image analysis. It is used in many content-based image retrieval systems, where it allows the identification of a larger number of images from distinct origins. This paper presents a novel approach for image analysis and retrieval based on complexity analysis. The approach consists of a texture segmentation step, performed by complexity analysis through BoxCounting fractal dimension, followed by the estimation of complexity of each computed region by multiscale fractal dimension. Experiments have been performed with MRI database in both pattern recognition and image retrieval contexts. Results show the accuracy of the method and also indicate how the performance changes as the texture segmentation process is altered.