User Relevance Research Articles

Gaze-Based Relevance Feedback for Realizing Region-Based Image Retrieval

In this paper, a gaze-based Relevance Feedback (RF) approach to region-based image retrieval is presented. Fundamental idea of the proposed method comprises the iterative estimation of the real-world objects (or their constituent parts) that are of interest to the user and the subsequent exploitation of this information for refining the image retrieval results. Primary novelties of this work are: a) the introduction of a new set of gaze features for realizing user's relevance assessment prediction at region-level, and b) the design of a time-efficient and effective object-based RF framework for image retrieval. Regarding the interpretation of the gaze signal, a novel set of features is introduced by formalizing the problem under a mathematical perspective, contrary to the exclusive use of explicitly defined features that are in principle derived from the psychology domain. Apart from the temporal attributes, the proposed features also represent the spatial characteristics of the gaze signal, which have not been extensively studied in the literature so far. On the other hand, the developed object-based RF mechanism aims at overcoming the main limitation of region-based RF approaches, i.e., the frequently inaccurate estimation of the regions of interest in the retrieved images. Moreover, the incorporation of a single-camera image processing-based gaze tracker makes the overall system cost efficient and portable. As it is shown by the experimental evaluation, the proposed method outperforms representative global- and region-based explicit RF approaches, using a challenging general-purpose image dataset.

Blosen: Blog Search Engine Based on Post Concept Clustering

This paper focuses on building a blog search engine which doesn't focus only on keyword search but includes extended search capabilities. It also incorporates the blog-post concept clustering which is based on the category extracted from the blog post semantic content analysis. The proposed approach is titled as "BloSen (Blog Search Engine)". It involves in extracting the posts from blogs and parsing them to extract the blog elements and store them as fields in a document format. Inverted index is being built on the fields of the documents. Search is induced on the index and requested query is processed based on the documents so far made from blog posts. It currently focuses on Blogger and Wordpress hosted blogs since both these hosting services are the most popular ones in the blogosphere. The proposed BloSen model is experimented with a prototype implementation and the results of the experiments with the user's relevance cumulative metric value of 95.44% confirms the efficiency of the proposed model.

Multi-Instance Learning for Image Retrieval with Relevance Feedback

In this paper, we propose a novel method for image retrieval based on multi-instance learning with relevance feedback. The process of this method mainly includes the following three steps: First, it segments each image into a number of regions, treats images and regions as bags and instances respectively. Second, it constructs an objective function of multi-instance learning with the query images, which is used to rank the images from a large digital repository according to the distance values between the nearest region vector of each image and the maximum of the objective function. Third, based on the users relevance feedback, several rounds may be needed to refine the output images and their ranks. Finally, a satisfying set of images will be returned to users. Experimental results on COREL image data sets have demonstrated the effectiveness of the proposed approach.

A 3D Engineering Model Retrieval Algorithm Based on Relevance Feedback and Features Combination

In order to reuse 3D models and design knowledge efficiently, a number of 3D model retrieval algorithms based on content features of models have been proposed in recent years. Although, the features-based methods have achieved some progress, there are two limitations stilly. The first, single content feature cant be suit for all kinds of 3D models; different features have different strengths and weakness. The second, semantic gap, the semantic of model is independent from low-level characteristics. For those two issues, we present a 3D engineering model retrieval algorithm based on relevance feedback and features combination in this paper. The proposed method takes advantage of multiple features by allying them with weights. In the retrieval process, our method utilizes the Particle Swarm Optimization to update the weights dynamically based on users relevance feedback information in order to narrowing the gap between high-level semantic knowledge and low-level content features. The Experiments, based on publicly available 3D model database Engineering Shape Benchmark (ESB) developed by Purdue University, suggested that the proposed approach has better retrieval ability than traditional ones.

Interactive Multiscale Classification of High-Resolution Remote Sensing Images

The use of remote sensing images (RSIs) as a source of information in agribusiness applications is very common. In those applications, it is fundamental to identify and understand trends and patterns in space occupation. However, the identification and recognition of crop regions in remote sensing images are not trivial tasks yet. In high-resolution image analysis and recognition, many of the problems are related to the representation scale of the data, and to both the size and the representativeness of the training set. In this paper, we propose a method for interactive classification of remote sensing images considering multiscale segmentation. Our aim is to improve the selection of training samples using the features from the most appropriate scales of representation. We use a boosting-based active learning strategy to select regions at various scales for user's relevance feedback. The idea is to select the regions that are closer to the border that separates both target classes: relevant and non-relevant regions. Experimental results showed that the combination of scales produces better results than isolated scales in a relevance feedback process. Furthermore, the interactive method achieved good results with few user interactions. The proposed method needs only a small portion of the training set to build classifiers that are as strong as the ones generated by a supervised method that uses the whole training set.

Your Relevance Feedback Is Essential: Enhancing the Learning to Rank Using the Virtual Feature Based Logistic Regression

Information retrieval applications have to publish their output in the form of ranked lists. Such a requirement motivates researchers to develop methods that can automatically learn effective ranking models. Many existing methods usually perform analysis on multidimensional features of query-document pairs directly and don't take users' interactive feedback information into account. They thus incur the high computation overhead and low retrieval performance due to an indefinite query expression. In this paper, we propose a Virtual Feature based Logistic Regression (VFLR) ranking method that conducts the logistic regression on a set of essential but independent variables, called virtual features (VF). They are extracted via the principal component analysis (PCA) method with the user's relevance feedback. We then predict the ranking score of each queried document to produce a ranked list. We systematically evaluate our method using the LETOR 4.0 benchmark datasets. The experimental results demonstrate that the proposal outperforms the state-of-the-art methods in terms of the Mean Average Precision (MAP), the Precision at position k (P@k), and the Normalized Discounted Cumulative Gain at position k (NDCG@k).

Query Representation through Lexical Association for Information Retrieval

A user query for information retrieval (IR) applications may not contain the most appropriate terms (words) as actually intended by the user. This is usually referred to as the term mismatch problem and is a crucial research issue in IR. Using the notion of relevance, we provide a comprehensive theoretical analysis of a parametric query vector, which is assumed to represent the information needs of the user. A lexical association function has been derived analytically using the system relevance criteria. The derivation is further justified using an empirical evidence from the user relevance criteria. Such analytical derivation as presented in this paper provides a proper mathematical framework to the query expansion techniques, which have largely been heuristic in the existing literature. By using the generalized retrieval framework, the proposed query representation model is equally applicable to the vector space model (VSM), Okapi best matching 25 (Okapi BM25), and Language Model (LM). Experiments over various data sets from TREC show that the proposed query representation gives statistically significant improvements over the baseline Okapi BM25 and LM as well as other well-known global query expansion techniques. Empirical results along with the theoretical foundations of the query representation confirm that the proposed model extends the state of the art in global query expansion.

Integrating Recognition and Retrieval With Relevance Feedback for Spoken Term Detection

Recognition and retrieval are typically viewed as two cascaded independent modules for spoken term detection (STD). Retrieval techniques are assumed to be applied on top of automatic speech recognition (ASR) output, with performance depending on ASR accuracy. We propose a framework that integrates recognition and retrieval and consider them jointly in order to yield better STD performance. This can be achieved either by adjusting the acoustic model parameters (model-based) or by considering detected examples (example-based) using relevance information provided by the user (user relevance feedback) or inferred by the system (pseudo-relevance feedback), either for a given query (short-term context) or by taking into account many previous queries (long-term context). Such relevance feedback approaches have long been used in text information retrieval, but are rarely considered and cannot be directly applied to the retrieval of spoken content. The proposed relevance feedback approaches are specific to spoken content retrieval and are hence very different from those developed for text retrieval, which are applied only to text symbols. We present not only these relevance feedback scenarios and approaches for STD, but also propose a framework to integrate them all together. Preliminary experiments showed significant improvements in each case.

통합 검색 환경에서 이용자 적합성 판단 기준에 관한 탐색적 연구

본 연구는 한국 통합 검색 환경에서의 이용자 적합성 판단 기준에 관한 탐색적 연구이다. 이를 위해 10명의 참가자들을 대상으로 반구조화(semi-structured) 인터뷰를 수행하여 데이터를 수집하였다. 참가자들은 네이버, 다음 등과 같은 통합 검색 환경에서 본인들이 관심 있거나 필요로 하는 다양한 검색을 수행하고, 그 과정에서 문서가 적합한지와 그 판단 기준에 대해 기술하였다. 연구 결과 8개의 적합성 판단 기준과 비적합성 판단 기준, 그리고 검색 환경이 변화하여도 이용자가 적합성을 판단하는 기준들이 크게 변화하지는 않지만 데이터 증가와 이용자 요구의 고도화로 특수성과 구체성이 중요한 적합성 판단 기준으로 부각되는 점을 발견하였다.

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 information retrieval system via modelling user behaviour

There has been an exponential growth in the volume and variety of information available on the Internet, similarly there has been a significant demand from users’ for accurate information that matches their interests, however, the two are often incompatible because of the effectiveness of retrieving the exact information the user requires. This paper addresses this problem with an adaptive agent-based modelling approach that relies on evolutionary user-modelling. The proposed information retrieval system learns user needs from user-provided relevance feedback. It is proposed that retrieval effectiveness can be improved by applying computational intelligence techniques for modelling information needs, through interactive reinforcement learning. The method combines qualitative (subjective) user relevance feedback with quantitative (algorithmic) measures of the relevance of retrieved documents. An adaptive information retrieval system is developed whose retrieval effectiveness is evaluated using traditional precision and recall.

Finding relevant semantic association paths through user-specific intermediate entities

AbstractSemantic Associations are complex relationships between entities over metadata represented in a RDF graph. While searching for complex relationships, it is possible to find too many relationships between entities. Therefore, it is important to locate interesting and meaningful relations and rank them before presenting to the end user. In recent years e-learning systems have become very popular in all fields of higher education. In an e-learning environment, user may expect to search the semantic relationship paths between two concepts or entities. There may be numerous relationships between two entities which involve more intermediate entities. In order to filter the size of results set based on user's relevance, user may introduce one or more known intermediate entities. In this paper, we present a Modified bidirectional Breadth-First-Search algorithm for finding paths between two entities which pass through other intermediate entities and the paths are ranked according to the users' needs. We have evaluated our system through empirical evaluation. We have compared the execution time to discover the paths between entities for our proposed search method and existing method. According to our experiments our proposed algorithm improves search efficiently. The average correlation coefficient between the proposed system ranking and the human ranking is 0.69. It explains that our proposed system ranking is highly correlated with human ranking.

Ethics, strategy and user relevance: The case of Google.cn (Response to: Google vs. China's “Great Firewall”: Ethical Implications for Free Speech and Sovereignty)

This article echoes the discussion of ethical issue regarding the withdrawal of Google.cn from Mainland China in early 2010 which was initially raised by the article “Google vs. China's ‘Great Firewall”. Based on our observations and analyses, we argue that neither Google.cn's entry of China market in 2006 nor its departure in 2010 was out of ethical consideration as proclaimed. Particularly, we conclude that Google was primarily politicizing its departure of China by accusing the information censorship rules of China as the main culprit.

User relevance criteria choices and the information search process

Relevance judgments occur within an information search process, where time, context and situation can impact the judgments. The determination of relevance is dependent on a number of factors and variables which include the criteria used to determine relevance. The relevance judgment process and the criteria used to make those judgments are manifestations of the cognitive changes which occur during the information search process. Understanding why these relevance criteria choices are made, and how they vary over the information search process can provide important information about the dynamic relevance judgment process. This information can be used to guide the development of more adaptive information retrieval systems which respond to the cognitive changes of users during the information search process. The research data analyzed here was collected in two separate studies which examined a subject’s relevance judgment over an information search process. Statistical analysis was used to examine these results and determine if there were relationships between criteria selections, relevance judgments, and the subject’s progression through the information search process. Findings confirm and extend findings of previous studies, providing strong statistical evidence of an association between the information search process and the choices of relevance criteria by users, and identifying specific changes in the user preferences for specific criteria over the course of the information search process.

Newsblog relevance: Applying relevance criteria to news-related blogs

Blogs, and particularly newsblogs, are receiving increased attention for their role in uncovering important or underdeveloped aspects of news stories. This research was an attempt to explore relevance criteria as it applies to users of newsblogs. An anonymous online questionnaire presented individuals with a list of established user relevance criteria, ranking the importance of each characteristic in determining a newsblog's quality.

Adaptive learning for relevance feedback: application to digital mammography.

With the rapid growing volume of images in medical databases, development of efficient image retrieval systems to retrieve relevant or similar images to a query image has become an active research area. Despite many efforts to improve the performance of techniques for accurate image retrieval, its success in biomedicine thus far has been quite limited. This article presents an adaptive content-based image retrieval (CBIR) system for improving the performance of image retrieval in mammographic databases. In this work, the authors propose a new relevance feedback approach based on incremental learning with support vector machine (SVM) regression. Also, the authors present a new local perturbation method to further improve the performance of the proposed relevance feedback system. The approaches enable efficient online learning by adapting the current trained model to changes prompted by the user's relevance feedback, avoiding the burden of retraining the CBIR system. To demonstrate the proposed image retrieval system, the authors used two mammogram data sets: A set of 76 mammograms scored based on geometrical similarity and a larger set of 200 mammograms scored by expert radiologists based on pathological findings. The experimental results show that the proposed relevance feedback strategy improves the retrieval precision for both data sets while achieving high efficiency compared to offline SVM. For the data set of 200 mammograms, the authors obtained an average precision of 0.48 and an area under the precision-recall curve of 0.79. In addition, using the same database, the authors achieved a high pathology matching rate greater than 80% between the query and the top retrieved images after relevance feedback. Using mammographic databases, the results demonstrate that the proposed approach is more accurate than the model without using relevance feedback not only in image retrieval but also in pathology matching while maintaining its effectiveness for online relevance feedback applications.

SU‐GG‐I‐153: A Novel Relevance Feedback Approach for Efficient Mammogram Image Retrieval

Purpose: With the rapid increase of medical databases and PACS (picture archiving and communication systems) image volumes, it became critical to develop new tools to retrieve images from these archives more effectively and efficiently for routine clinical practice. A key factor for successful image retrieval systems lies in the development of efficient methods that can derive high‐level semantic concepts from low‐level image content features. Despite many efforts, an efficient retrieval system for medical images still remains a challenging problem due to their complex contents in relation to disease conditions. Towards this goal we have developed a machine learning framework that utilizes expert's knowledge for online retrieval of relevant mammogram images based on clustered microcalcifications. Method and Materials: We tested a database of 200 clinical mammograms that were taken from 104 patients with known pathology. To automatically retrieve mammograms from the database that are perceptually similar to a given query image, we proposed an efficient relevance feedback algorithm based on support vector machine (SVM) regression and incremental learning that dramatically reduces the processing time, avoiding the retraining of the retrieval system. Results: We conducted various experiments to evaluate the developed algorithm under varying user's interactions. It was observed that our proposed method showed significant superiority over off‐line learning. In particular, in a matching fraction test where the top‐k retrieved images were compared with the disease condition of the query image (benign or malignant) after the user's relevance feedback, the proposed method achieved a matching fraction as high as 82.4%. Conclusions: We have developed a novel relevance feedback method for effective retrieval of relevant mammogram images. Our experimental results demonstrated that the proposed method is not only better in performance, but more computationally efficient than offline SVM based on re‐training, while maintaining its effectiveness for online clinical application.

The LAILAPS Search Engine: Relevance Ranking in Life Science Databases

SummarySearch engines and retrieval systems are popular tools at a life science desktop. The manual inspection of hundreds of database entries, that reflect a life science concept or fact, is a time intensive daily work. Hereby, not the number of query results matters, but the relevance does. In this paper, we present the LAILAPS search engine for life science databases. The concept is to combine a novel feature model for relevance ranking, a machine learning approach to model user relevance profiles, ranking improvement by user feedback tracking and an intuitive and slim web user interface, that estimates relevance rank by tracking user interactions. Queries are formulated as simple keyword lists and will be expanded by synonyms. Supporting a flexible text index and a simple data import format, LAILAPS can easily be used both as search engine for comprehensive integrated life science databases and for small in-house project databases.With a set of features, extracted from each database hit in combination with user relevance preferences, a neural network predicts user specific relevance scores. Using expert knowledge as training data for a predefined neural network or using users own relevance training sets, a reliable relevance ranking of database hits has been implemented.In this paper, we present the LAILAPS system, the concepts, benchmarks and use cases. LAILAPS is public available for SWISSPROT data at http://lailaps.ipk-gatersleben.de

The LAILAPS search engine: relevance ranking in life science databases.

Search engines and retrieval systems are popular tools at a life science desktop. The manual inspection of hundreds of database entries, that reflect a life science concept or fact, is a time intensive daily work. Hereby, not the number of query results matters, but the relevance does. In this paper, we present the LAILAPS search engine for life science databases. The concept is to combine a novel feature model for relevance ranking, a machine learning approach to model user relevance profiles, ranking improvement by user feedback tracking and an intuitive and slim web user interface, that estimates relevance rank by tracking user interactions. Queries are formulated as simple keyword lists and will be expanded by synonyms. Supporting a flexible text index and a simple data import format, LAILAPS can easily be used both as search engine for comprehensive integrated life science databases and for small in-house project databases. With a set of features, extracted from each database hit in combination with user relevance preferences, a neural network predicts user specific relevance scores. Using expert knowledge as training data for a predefined neural network or using users own relevance training sets, a reliable relevance ranking of database hits has been implemented. In this paper, we present the LAILAPS system, the concepts, benchmarks and use cases. LAILAPS is public available for SWISSPROT data at http://lailaps.ipk-gatersleben.de.

Predicting DataSpace Retrieval Using Probabilistic Hidden Information

This paper discusses the issues involved in the design of a complete information retrieval system for DataSpace based on user relevance probabilistic schemes. First, Information Hidden Model (IHM) is constructed taking into account the users' perception of similarity between documents. The system accumulates feedback from the users and employs it to construct user oriented clusters. IHM allows integrating uncertainty over multiple, interdependent classifications and collectively determines the most likely global assignment. Second, Three different learning strategies are proposed, namely query-related UHH, UHB and UHS (User Hidden Habit, User Hidden Background, and User Hidden keyword Semantics) to closely represent the user mind. Finally, the probability ranking principle shows that optimum retrieval quality can be achieved under certain assumptions. An optimization algorithm to improve the effectiveness of the probabilistic process is developed. We first predict the data sources where the query results could be found. Therefor, compared with existing approaches, our precision of retrieval is better and do not depend on the size and the DataSpace heterogeneity.