Multimodal retrieval with relevance feedback based on genetic programming

This paper presents a framework for multimodal retrieval with relevance feedback based on genetic programming. In this supervised learning-to-rank framework, genetic programming is used for the discovery of effective combination functions of (multimodal) similarity measures using the information obtained throughout the user relevance feedback iterations. With these new functions, several similarity measures, including those extracted from different modalities (e.g., text, and content), are combined into one single measure that properly encodes the user preferences. This framework was instantiated for multimodal image retrieval using visual and textual features and was validated using two image collections, one from the Washington University and another from the ImageCLEF Photographic Retrieval Task. For this image retrieval instance several multimodal relevance feedback techniques were implemented and evaluated. The proposed approach has produced statistically significant better results for multimodal retrieval over single modality approaches and superior effectiveness when compared to the best submissions of the ImageCLEF Photographic Retrieval Task 2008.     

Adaptive lifting for shape-based image retrieval

We propose to use adaptive wavelet lifting for image retrieval systems that are based on shape detection and multiresolution structures of objects in a database against a background of texture. To measure the performance of our approach, feature vectors are computed based on moment invariants of detail coefficients produced by the adaptive lifting scheme and retrieval rates are obtained by measuring distances between these vectors. Retrieval rates are compared with the rates obtained when using non-adaptive wavelet filtering as a preprocessing step. A synthetic database is created for this simulation.     

Multi‐level reranking approach for bug localization

Bug fixing has a key role in software quality evaluation. Bug fixing starts with the bug localization step, in which developers use textual bug information to find location of source codes which have the bug. Bug localization is a tedious and time consuming process. Information retrieval requires understanding the programme's goal, coding structure, programming logic and the relevant attributes of bug. Information retrieval (IR) based bug localization is a retrieval task, where bug reports and source files represent the queries and documents, respectively. In this paper, we propose BugCatcher, a newly developed bug localization method based on multi‐level re‐ranking IR technique. We evaluate BugCatcher on three open source projects with approximately 3400 bugs. Our experiments show that multi‐level reranking approach to bug localization is promising. Retrieval performance and accuracy of BugCatcher are better than current bug localization tools, and BugCatcher has the best Top N, Mean Average Precision (MAP) and Mean Reciprocal Rank (MRR) values for all datasets.     

基于内容的图像检索系统性能评价

在图像检索需求多元化和专业化的推动下,CBIR技术日趋成熟,目前已有越来越多的商用和科研系统相继推出。这样就迫切需要展开对CBIR系统性能评价标准的研究,因为任何一项技术都是由该领域中相应的评价标准来推动的。为了使人们对这方面的现状动态有一概略了解,首先讨论了基于内容的图像检索评价过程中的两个基本问题,即大规模数据库的建立和获取进行相关性评判,然后对近年来文献中所见的基于内容的图像检索系统性能评价方法进行了回顾和综述;最后在此基础上,对其发展方向进行了探讨,并提出建立一个标准的测试数据集用来推动基于内容的图像检索系统性能评价的发展和更好地将人结合到基于内容的图像检索系统的性能评价过程中,以发展交互式的性能评价方法的建议。     

On stability of signature-based similarity measures for content-based image retrieval

Retrieving similar images from large image databases is a challenging task for today’s content-based retrieval systems. Aiming at high retrieval performance, these systems frequently capture the user’s notion of similarity through expressive image models and adaptive similarity measures. On the query side, image models can significantly differ in quality compared to those stored on the database side. Thus, similarity measures have to be robust against these individual quality changes in order to maintain high retrieval performance. In this paper, we investigate the robustness of the family of signature-based similarity measures in the context of content-based image retrieval. To this end, we introduce the generic concept of average precision stability, which measures the stability of a similarity measure with respect to changes in quality between the query and database side. In addition to the mathematical definition of average precision stability, we include a performance evaluation of the major signature-based similarity measures focusing on their stability with respect to querying image databases by examples of varying quality. Our performance evaluation on recent benchmark image databases reveals that the highest retrieval performance does not necessarily coincide with the highest stability.     

基于内容图像检索中的特征性能评价

在基于内容的图像检索中，不同图像特征反映了图像各个侧面的内在特性，因此，在使用图像特征进行检索时存在多种相似性度量方法．特征以及特征间相似性度量方法的选取是当前CBIR研究的一个重要课题．评估了CBIR系统中使用的图像特征在不同相似性度量方法下及多种特征在不同图像库上的检索性能，为CBIR系统的设计和实现提供一定的依据．通过实验发现，图像特征的检索性能不仅同相似性度量方法有关系，同时与图像库也有密切的关系．     

An Efficient Deep Learning-based Content-based Image Retrieval Framework

The use of massive image databases has increased drastically over the few years due to evolution of multimedia technology. Image retrieval has become one of the vital tools in image processing applications. Content-Based Image Retrieval (CBIR) has been widely used in varied applications. But, the results produced by the usage of a single image feature are not satisfactory. So, multiple image features are used very often for attaining better results. But, fast and effective searching for relevant images from a database becomes a challenging task. In the previous existing system, the CBIR has used the combined feature extraction technique using color auto-correlogram, Rotation-Invariant Uniform Local Binary Patterns (RULBP) and local energy. However, the existing system does not provide significant results in terms of recall and precision. Also, the computational complexity is higher for the existing CBIR systems. In order to handle the above mentioned issues, the Gray Level Co-occurrence Matrix (GLCM) with Deep Learning based Enhanced Convolution Neural Network (DLECNN) is proposed in this work. The proposed system framework includes noise reduction using histogram equalization, feature extraction using GLCM, similarity matching computation using Hierarchal and Fuzzy c- Means (HFCM) algorithm and the image retrieval using DLECNN algorithm. The histogram equalization has been used for computing the image enhancement. This enhanced image has a uniform histogram. Then, the GLCM method has been used to extract the features such as shape, texture, colour, annotations and keywords. The HFCM similarity measure is used for computing the query image vector's similarity index with every database images. For enhancing the performance of this image retrieval approach, the DLECNN algorithm is proposed to retrieve more accurate features of the image. The proposed GLCM+DLECNN algorithm provides better results associated with high accuracy, precision, recall, f-measure and lesser complexity. From the experimental results, it is clearly observed that the proposed system provides efficient image retrieval for the given query image.     

综合颜色特征的彩色图像检索方法

基于内容的图像检索技术已成为当前的研究热点,文章提出了一种综合利用两种颜色特征进行图像检索的新方法。首先,在变换空间建立色度直方图表示图像的颜色分布特征。为进行图像间的相似性度量,对Swain定义的直方图相似性度量作了改进,为弥补全局直方图不包含颜色空间分布关系的缺点,文章提取了另一种颜色特征,即分块的颜色矩,其距离度量为特征矢量的比值相似度。最后,综合利用上述两个特征对图像进行共同检索。通过对真实图像数据的检索实验表明:综合两种特征检索图像比单一特征检索效果更好。     

An effective image retrieval mechanism using family-based spatial consistency filtration with object region

How to construct an appropriate spatial consistent measurement is the key to improving image retrieval performance. To address this problem, this paper introduces a novel image retrieval mechanism based on the family filtration in object region. First, we supply an object region by selecting a rectangle in a query image such that system returns a ranked list of images that contain the same object, retrieved from the corpus based on 100 images, as a result of the first rank. To further improve retrieval performance, we add an efficient spatial consistency stage, which is named family-based spatial consistency filtration, to re-rank the results returned by the first rank. We elaborate the performance of the retrieval system by some experiments on the dataset selected from the key frames of ``TREC Video Retrieval Evaluation 2005 (TRECVID2005)'. The results of experiments show that the retrieval mechanism proposed by us has vast major effect on the retrieval quality. The paper also verifies the stability of the retrieval mechanism by increasing the number of images from 100 to 2000 and realizes generalized retrieval with the object outside the dataset.     

图象检索算子开放测试平台T-Brief设计与实现

基于内容的图象检索是近年来多媒体技术领域发展的一个热点之一,大量基于特征的检索算法不断涌现。该文介绍一个对算法开放的抽取特征检索图象的算法测试平台,该平台可以即时集成现有的多种不同算法,并便于管理,同时它还提供了诸如综合检索,渐进检索等功能,可用于算法研究,性能比较等。     

图像检索中的相似性判别及索引方法综述

基于内容的图像检索(CBIR)是对传统信息检索领域的扩展.它采用图像视觉内容的相似性判别进行查询.CBIR涉及到很多科学领域的课题.本文则仅主要综述CBIR技术中的相似性度量方法,索引方式,以及检索性能的评价.最后,分析了该领域现存的问题、最新研究动态及发展方向.     

基于分块主色法的图像检索算法研究

基于内容的图像检索CBIR(Content Based Image Retrieval)是当前多媒体检索的热点。本文提出了一种基于图像的分块主颜色的图像检索算法,论述了系统的结构、颜色特征提取方法及其相似匹配方法,并给出部分实验结果。从实验结果来看,文中提出的把图像分块再提取各分块的主色的方法,使得该系统获得了良好的检索效果。     

A criterion for optimizing kernel parameters in KBDA for image retrieval.

A criterion is proposed to optimize the kernel parameters in Kernel-based Biased Discriminant Analysis (KBDA) for image retrieval. Kernel parameter optimization is performed by optimizing the kernel space such that the positive images are well clustered while the negative ones are pushed far away from the positives. The proposed criterion measures the goodness of a kernel space, and the optimal kernel parameter set is obtained by maximizing this criterion. Retrieval experiments on two benchmark image databases demonstrate the effectiveness of proposed criterion for KBDA to achieve the best possible performance at the cost of a small fractional computational overhead.     

基于模糊相似度量的图像检索

提出一种基于Sugeno模糊积分的模糊相似度量并用于图像检索中。文章用模糊测度来描述人的主观反馈。试验表明该文的方法可以大大提高图像检索系统的效率和稳定性,在反馈后的表现要优于加权平均方法(WAO)和采用Choquet积分(CI)的方法。     

综合颜色和纹理特征的图像检索算法

基于内容的图像检索是当前多媒体信息检索的热点之一。基于内容的图像检索技术是根据对图像内容(特征)的描述和提取,在图像库中找到具有指定内容(特征)的图像。本文对图像颜色特征和纹理特征的提取、相似性度量等基于内容的图像检索的关键技术进行了分析和研究,并在此基础上,提出了一个基于颜色特征和纹理特征的图像检索算法并验证了其有效性。该算法采用HSV颜色空间的直方图作为颜色特征向量,采用灰度共生矩阵的四个纹理特征:能量、熵、惯性矩和相关性构成纹理特征向量,采用欧氏距离进行相似性度量。实验结果表明,该算法实现的系统具有良好的图像检索功能。