Relevance feedback between hypertext and Semantic Web search: Frameworks and evaluation

We investigate the possibility of using Semantic Web data to improve hypertext Web search. In particular, we use relevance feedback to create a ‘virtuous cycle’ between data gathered from the Semantic Web of Linked Data and web-pages gathered from the hypertext Web. Previous approaches have generally considered the searching over the Semantic Web and hypertext Web to be entirely disparate, indexing, and searching over different domains. While relevance feedback has traditionally improved information retrieval performance, relevance feedback is normally used to improve rankings over a single data-set. Our novel approach is to use relevance feedback from hypertext Web results to improve Semantic Web search, and results from the Semantic Web to improve the retrieval of hypertext Web data. In both cases, an evaluation is performed based on certain kinds of informational queries (abstract concepts, people, and places) selected from a real-life query log and checked by human judges. We evaluate our work over a wide range of algorithms and options, and show it improves baseline performance on these queries for deployed systems as well, such as the Semantic Web Search engine FALCON-S and Yahoo! Web search. We further show that the use of Semantic Web inference seems to hurt performance, while the pseudo-relevance feedback increases performance in both cases, although not as much as actual relevance feedback. Lastly, our evaluation is the first rigorous ‘Cranfield’ evaluation of Semantic Web search.     

Active learning paradigms for CBIR systems based on optimum-path forest classification

This paper discusses methods for content-based image retrieval (CBIR) systems based on relevance feedback according to two active learning paradigms, named greedy and planned. In greedy methods, the system aims to return the most relevant images for a query at each iteration. In planned methods, the most informative images are returned during a few iterations and the most relevant ones are only presented afterward. In the past, we proposed a greedy approach based on optimum-path forest classification (OPF) and demonstrated its gain in effectiveness with respect to a planned method based on support-vector machines and another greedy approach based on multi-point query. In this work, we introduce a planned approach based on the OPF classifier and demonstrate its gain in effectiveness over all methods above using more image databases. In our tests, the most informative images are better obtained from images that are classified as relevant, which differs from the original definition. The results also indicate that both OPF-based methods require less user involvement (efficiency) to satisfy the user's expectation (effectiveness), and provide interactive response times.     

Enhancing passage retrieval in log files by query expansion based on explicit and pseudo relevance feedback

Passage retrieval is usually defined as the task of searching for passages which may contain the answer for a given query. While these approaches are very efficient when dealing with texts, applied to log files (i.e. semi-structured data containing both numerical and symbolic information) they usually provide irrelevant or useless results. Nevertheless one appealing way for improving the results could be to consider query expansions that aim at adding automatically or semi-automatically additional information in the query to improve the reliability and accuracy of the returned results. In this paper, we present a new approach for enhancing the relevancy of queries during a passage retrieval in log files. It is based on two relevance feedback steps. In the first one, we determine the explicit relevance feedback by identifying the context of the requested information within a learning process. The second step is a new kind of pseudo relevance feedback. Based on a novel term weighting measure it aims at assigning a weight to terms according to their relatedness to queries. This measure, called TRQ (Term Relatedness to Query), is used to identify the most relevant expansion terms.The main advantage of our approach is that is can be applied both on log files and documents from general domains. Experiments conducted on real data from logs and documents show that our query expansion protocol enables retrieval of relevant passages.     

基于压缩图像的K均值优化聚类相关反馈

在传统的基于内容的图像检索方法中,直接采集原始格式的图像检索比较多,由于数据量大,给存储或传输带来不便.基于小波变换和粒子群的K值聚类相关反馈的图像检索优点在于解决了数据量大、省略解压缩环节、特征向量包含在压缩域检索系数中;并克服了传统的K均值算法易陷入局部极小值的缺点.实验结果表明,图像压缩后的基于粒子群的K均值聚类提高了检索效率.     

基于熵和块匹配的图像检索技术

提出了一种图像熵和特征块匹配相结合的图像检索方法.为了提高图像的检索精度和效率,首先用计算图像熵并与设定的闭值比较实现对图像库的预分类;然后利用Harris算子检测出图像的特征点,用以特征点为中心的特征块的前三阶颜色矩来描述特征块的特征;进一步统计出两个图像中匹配的特征块数目,计算图像间的相似距离并进行仿真.仿真结果表明,算法中所使用的特征块更全面、更精确地描述了图像的视觉信息,实现相似度计算的方法简单和高效,证明分级检索方法在保证图像检索效率的前提下,极大地缩短了检索时间.     

Image retrieval model based on weighted visual features determined by relevance feedback

An accurate and rapid method is required to retrieve the overwhelming majority of digital images. To date, image retrieval methods include content-based retrieval and keyword-based retrieval, the former utilizing visual features such as color and brightness, and the latter utilizing keywords that describe the image. However, the effectiveness of these methods in providing the exact images the user wants has been under scrutiny. Hence, many researchers have been working on relevance feedback, a process in which responses from the user are given as feedback during the retrieval session in order to define a user’s need and provide an improved result. Methods that employ relevance feedback, however, do have drawbacks because several pieces of feedback are necessary to produce an appropriate result, and the feedback information cannot be reused. In this paper, a novel retrieval model is proposed, which annotates an image with keywords and modifies the confidence level of the keywords in response to the user’s feedback. In the proposed model, not only the images that have been given feedback, but also other images with visual features similar to the features used to distinguish the positive images are subjected to confidence modification. This allows for modification of a large number of images with relatively little feedback, ultimately leading to faster and more accurate retrieval results. An experiment was performed to verify the effectiveness of the proposed model, and the result demonstrated a rapid increase in recall and precision using the same amount of feedback.     

Relevance feedback for category search in music retrieval based on semantic concept learning

Traditional content-based music retrieval systems retrieve a specific music object which is similar to what a user has requested. However, the need exists for the development of category search for the retrieval of a specific category of music objects which share a common semantic concept. The concept of category search in content-based music retrieval is subjective and dynamic. Therefore, this paper investigates a relevance feedback mechanism for category search of polyphonic symbolic music based on semantic concept learning. For the consideration of both global and local properties of music objects, a segment-based music object modeling approach is presented. Furthermore, in order to discover the user semantic concept in terms of discriminative features of discriminative segments, a concept learning mechanism based on data mining techniques is proposed to find the discriminative characteristics between relevant and irrelevant objects. Moreover, three strategies, the Most-Positive, the Most-Informative, and the Hybrid, to return music objects concerning user relevance judgments are investigated. Finally, comparative experiments are conducted to evaluate the effectiveness of the proposed relevance feedback mechanism. Experimental results show that, for a database of 215 polyphonic music objects, 60% average precision can be achieved through the use of the proposed relevance feedback mechanism.

Relevance feedback for real-world human action retrieval

Content-based video retrieval is an increasingly popular research field, in large part due to the quickly growing catalogue of multimedia data to be found online. Even though a large portion of this data concerns humans, however, retrieval of human actions has received relatively little attention. Presented in this paper is a video retrieval system that can be used to perform a content-based query on a large database of videos very efficiently. Furthermore, it is shown that by using ABRS-SVM, a technique for incorporating Relevance feedback (RF) on the search results, it is possible to quickly achieve useful results even when dealing with very complex human action queries, such as in Hollywood movies.     

一种图像背景视觉语义模板的生成方法

1.引言图像低层的物理视觉特征与人的高层认识之间不存在明显的直接联系,这就是视觉信息处理中的“语义鸿沟,这使得基于图像全局特征的检索结果与人的主观感觉大相径庭。要缓解“语义鸿沟”问题,一个直接的方法是在低层的视觉特征和高层的主观语义之间建立多个中间处理过程,使得两者能够有个渐进的过渡。这种分而治之的策略,需要保证每一步处理结果都要更加有利于主观语义的辨认,同时这些处理     

Bayesian relevance feedback for content-based image retrieval

Despite the efforts to reduce the so-called semantic gap between the user's perception of image similarity and the feature-based representation of images, the interaction with the user remains fundamental to improve performances of content-based image retrieval systems. To this end, relevance feedback mechanisms are adopted to refine image-based queries by asking users to mark the set of images retrieved in a neighbourhood of the query as being relevant or not. In this paper, the Bayesian decision theory is used to estimate the boundary between relevant and non-relevant images. Then, a new query is computed whose neighbourhood is likely to fall in a region of the feature space containing relevant images. The performances of the proposed query shifting method have been compared with those of other relevance feedback mechanisms described in the literature. Reported results show the superiority of the proposed method.     

An attempt to enhance performance in user session based information retrieval

The ongoing surge in the amount of online information has made the process of accurate retrieval much more difficult. Providers of information retrieval systems have come under a lot of pressure to improve their techniques to cater for the modern user. Conventional systems are often limited as they fail to understand the true search intent of the user. This is usually a result of both poor query formulation by the user and an inability of the search engine to process the query adequately. In this paper, an approach is presented that attempts to learn a user’s short-term interests through the clustering of their search results. A profile is maintained for each user to assist in the process of context resolution for a given query. The details of such an approach and experimental results to evaluate its effectiveness are presented in this paper.     

一种使用Harris特征点的区域图像检索算法

为了克服图像分割技术的限制，提出了一种基干特征点匹配技术的图像检索算法。手工提取图像中的一块区域作为查询图像，然后使用Harris算子提取彩色特征点，并用相应的颜色特征对特征点进行表示，最后利用特征点匹配技术实现区域图像的检索。实验表明，该方法对于图像的亮度变化和几何变换具有很强的鲁棒性，可以有效提高检索准确率。     

Exploring statistical correlations for image retrieval

Bridging the cognitive gap in image retrieval has been an active research direction in recent years, of which a key challenge is to get enough training data to learn the mapping functions from low-level feature spaces to high-level semantics. In this paper, image regions are classified into two types: key regions representing the main semantic contents and environmental regions representing the contexts. We attempt to leverage the correlations between types of regions to improve the performance of image retrieval. A Context Expansion approach is explored to take advantages of such correlations by expanding the key regions of the queries using highly correlated environmental regions according to an image thesaurus. The thesaurus serves as both a mapping function between image low-level features and concepts and a store of the statistical correlations between different concepts. It is constructed through a data-driven approach which uses Web data (images, their surrounding textual annotations) as training data source to learn the region concepts and to explore the statistical correlations. Experimental results on a database of 10,000 general-purpose images show the effectiveness of our proposed approach in both improving search precision (i.e. filter irrelevant images) and recall (i.e. retrieval relevant images whose context may be varied). Several major factors which have impact on the performance of our approach are also studied.     

Speed up interactive image retrieval

In multimedia retrieval, a query is typically interactively refined towards the “optimal” answers by exploiting user feedback. However, in existing work, in each iteration, the refined query is re-evaluated. This is not only inefficient but fails to exploit the answers that may be common between iterations. Furthermore, it may also take too many iterations to get the “optimal” answers. In this paper, we introduce a new approach called OptRFS (optimizing relevance feedback search by query prediction) for iterative relevance feedback search. OptRFS aims to take users to view the “optimal” results as fast as possible. It optimizes relevance feedback search by both shortening the searching time during each iteration and reducing the number of iterations. OptRFS predicts the potential candidates for the next iteration and maintains this small set for efficient sequential scan. By doing so, repeated candidate accesses (i.e., random accesses) can be saved, hence reducing the searching time for the next iteration. In addition, efficient scan on the overlap before the next search starts also tightens the search space with smaller pruning radius. As a step forward, OptRFS also predicts the “optimal” query, which corresponds to “optimal” answers, based on the early executed iterations’ queries. By doing so, some intermediate iterations can be saved, hence reducing the total number of iterations. By taking the correlations among the early executed iterations into consideration, OptRFS investigates linear regression, exponential smoothing and linear exponential smoothing to predict the next refined query so as to decide the overlap of candidates between two consecutive iterations. Considering the special features of relevance feedback, OptRFS further introduces adaptive linear exponential smoothing to self-adjust the parameters for more accurate prediction. We implemented OptRFS and our experimental study on real life data sets show that it can reduce the total cost of relevance feedback search significantly. Some interesting features of relevance feedback search are also discovered and discussed.     

Ego-similarity measurement for relevance feedback

Relevance Feedback in Content-Based Image Retrieval is an active field of research. Many mechanisms of Relevance Feedback exist with many interactive techniques and implement criteria. In this paper, we proposed a novel approach of RF which can set adaptive weights of similarity measurement for each database image from the user feedback, i.e. ego-similarity measurement. We would explore the feedback records were archived in the two different ways that stored along with query images (QRF-based) or along with each retrieved relevant image from the image database (DBRF-based). In the experiment, DBRF-based relevant feedback improved greatly in the retrieval effectiveness.