Image retrieval with automatic query expansion based on local analysis in a semantical concept feature space

The paper describes a user study examining methods for improving users queries, specifically interactive and automatic query expansion and advanced search options. The user study includes subjective and objective evaluation of the effect of the above methods and a comparison between the real and perceived effect.

Much attention has been paid to the relative effectiveness of Interactive Query Expansion (IQE) versus Automatic Query Expansion (AQE). This research has been shown that automatic query expansion (collection dependent) strategy gives better performance than no query expansion. The percentage of queries that are improved by AQE strategy is 57% with average precision equal to 43.2. Compared against AQE (collection dependent) strategy, IQE gives better average precision than AQE strategy. The percentage of queries that are improved by best IQE decision is 86% with average precision equal to 44.1. Evaluation process reveals that the value of n in AQE strategy that gave the optimal value of average precision for the whole query set is equal to one.

Research and experimentation using Arabic WordNet in the field of information retrieval are relatively new. It is limited compared to the research that has been done using Princeton WordNet. This work attempts to study the impact of Arabic WordNet on the performance of Arabic information retrieval. We extend Lucene with Arabic WordNet to expand user's queries. The major contribution of this study is to propose an interactive query expansion (IQE) methodology using the word's part-of-speech, according to the part it plays in a query. First, the user selects the appropriate part of speech for each term in the original query, and then he reselects the appropriate synonyms. Experimental results show that our IQE strategy produces a good Mean Average Precision (MAP), it is able to improve MAP by 12.6%, but no variant of automatic query expansion (AQE) strategies did. Nevertheless, the experiments allow us to conclude that with an appropriate use of Arabic WordNet as a source of linguistic information for AQE can improve effectiveness for Arabic information retrieval.

The steady growth in the size of textual document collections is a key progress-driver for modern information retrieval techniques whose effectiveness and efficiency are constantly challenged. Given a user query, the number of retrieved documents can be overwhelmingly large, hampering their efficient exploitation by the user. In addition, retaining only relevant documents in a query answer is of paramount importance for an effective meeting of the user needs. In this situation, the query expansion technique offers an interesting solution for obtaining a complete answer while preserving the quality of retained documents. This mainly relies on an accurate choice of the added terms to an initial query. Interestingly enough, query expansion takes advantage of large text volumes by extracting statistical information about index terms co-occurrences and using it to make user queries better fit the real information needs. In this respect, a promising track consists in the application of data mining methods to the extraction of dependencies between terms. In this paper, we present a novel approach for mining knowledge supporting query expansion that is based on association rules. The key feature of our approach is a better trade-off between the size of the mining result and the conveyed knowledge. Thus, our association rules mining method implements results from Galois connection theory and compact representations of rules sets in order to reduce the huge number of potentially useful associations. An experimental study has examined the application of our approach to some real collections, whereby automatic query expansion has been performed. The results of the study show a significant improvement in the performances of the information retrieval system, both in terms of recall and precision, as highlighted by the carried out significance testing using the Wilcoxon test.

This paper presents an image representation and matching framework for image categorization in medical image archives. Categorization enables one to determine automatically, based on the image content, the examined body region and imaging modality. It is a basic step in content-based image retrieval (CBIR) systems, the goal of which is to augment text-based search with visual information analysis. CBIR systems are currently being integrated with picture archiving and communication systems for increasing the overall search capabilities and tools available to radiologists. The proposed methodology is comprised of a continuous and probabilistic image representation scheme using Gaussian mixture modeling (GMM) along with information-theoretic image matching via the Kullback-Leibler (KL) measure. The GMM-KL framework is used for matching and categorizing X-ray images by body regions. A multidimensional feature space is used to represent the image input, including intensity, texture, and spatial information. Unsupervised clustering via the GMM is used to extract coherent regions in feature space that are then used in the matching process. A dominant characteristic of the radiological images is their poor contrast and large intensity variations. This presents a challenge to matching among the images, and is handled via an illumination-invariant representation. The GMM-KL framework is evaluated for image categorization and image retrieval on a dataset of 1500 radiological images. A classification rate of 97.5% was achieved. The classification results compare favorably with reported global and local representation schemes. Precision versus recall curves indicate a strong retrieval result as compared with other state-of-the-art retrieval techniques. Finally, category models are learned and results are presented for comparing images to learned category models.

In this work, a novel system, which works on a multidimensional feature space and detects the prominent signal components on the spectrum with high recall and precision, is presented. The boundary hiper planes are determined by using Support Vector Machine (SVM) classifier in the multidimensional feature space. The solution to the class training set imbalance problem is investigated with multiple SVM classifiers system. By balancing the training sets by random selections, the detection performance is raised. The features that are used in the proposed system are evaluated by Forward Feature Selection algorithm with two different selection criteria. The implemented system is compared to three different thresholding based detection system and it is observed that it has better performance than thresholding based systems. It is shown with proper test sets that it can perform high performance prominent component detection highly independent of training data.

A new approach for query expansion using Wikipedia and WordNet

Web queries clustering attract great concern recently. It is crucial for capturing frequently asked questions in question-answering system, most popular topics in search engine or automatic query expansion in information retrieval. The paper presents a new algorithm of web queries clustering using user click information in the query logs and applies it into query expansion. Different from previous work, in the new algorithm both word form and semantic information have been taken into considered. Experiments show that the new algorithm works effectively.

This paper presents a classification-driven biomedical image retrieval system to bride the semantic gap by transforming image features to their global categories at different granularity, such as image modality, body part, and orientation. To generate the feature vectors at different levels of abstraction, both the visual concept feature based on the bag of concepts model that comprise of local color and texture patches and various low-level global color, edge, and texture-related features are extracted. Since, it is difficult to find a unique feature to compare images effectively for all types of queries, we utilize a similarity fusion approach based on the linear combination of individual features. However, instead of using the commonly used fixed or hard weighting approach, we rely on the image classification to determine the importance of a feature at real time. For this, a supervised multi-class classifier based on the support vector machine (SVM) is trained on a set of sample images and classifier combination techniques based on the rules derived from the Bayes's theorem are explored. After the combined prediction of the classifiers for a query image category, the individual pre-computed weights of different features are adjusted in the similarity matching function for effective query-specific retrieval. Experiment is performed in a diverse medical image collection of 67,000 images of different modalities. It demonstrates the effectiveness of the category-specific similarity fusion approach with a mean average precision (MAP) score of 0.0265 when compared to using only a single feature or equal weighting of each feature in similarity matching.

System performance assessment and comparison are fundamental for large-scale image search engine development. This article documents a set of comprehensive empirical studies to explore the effects of multiple query evidences on large-scale social image search. The search performance based on the social tags, different kinds of visual features and their combinations are systematically studied and analyzed. To quantify the visual query complexity, a novel quantitative metric is proposed and applied to assess the influences of different visual queries based on their complexity levels. Besides, we also study the effects of automatic text query expansion with social tags using a pseudo relevance feedback method on the retrieval performance. Our analysis of experimental results shows a few key research findings: (1) social tag-based retrieval methods can achieve much better results than content-based retrieval methods; (2) a combination of textual and visual features can significantly and consistently improve the search performance; (3) the complexity of image queries has a strong correlation with retrieval results’ quality—more complex queries lead to poorer search effectiveness; and (4) query expansion based on social tags frequently causes search topic drift and consequently leads to performance degradation.

The paper focus on managing term selection task for PRF-based automatic query expansion. The proposed automatic query expansion approach is based on document clustering along with usage of new fuzzy inference system designed to combine various term selection measures. An integration of efficient document clustering algorithm and hybrid term selection method makes this approach better than other AQE methods. The performance of proposed approach and other AQE methods is tested on sets of queries for CACM and CISI datasets. The higher values obtained for different information retrieval performance measures prove superiority of proposed AQE approach over other AQE methods.KeywordsAutomatic query expansionTerm selectionFuzzy logicDocument clusteringF-measure

Most effective particular object and image retrieval approaches are based on the bag-of-words (BoW) model, and all state-of-the-art performance mainly involves a query expansion procedure, which is able to significantly improve retrieval results. Nowadays, Convolutional Neural Network(CNN) is widely applied in computer vision field, including image classification, caption, recognition and retrieval, etc. We introduce an extension to query expansion: an automatic method to select good candidate samples for interactive annotation which is used in query expansion using both BoW method and CNN feature. In this work, we address the query expansion framework using active learning, where the main focus is on the sample selection step in the process of query expansion. More specifically, we propose an active sample selection algorithm based on binary relevance classification, based on the assumption that most confusing samples of the classifiers have high probability to contain helpful true positives for query expansion, which significantly improves the retrieval performance. It takes full use of the multimodal information of the shortlist obtained from the basic retrieval to train a binary relevance classifier, which is used to pick up the most confusing samples for human annotation, with top list as unlabeled data and bottom list as fake negatives. And it can achieve a faster and better retrieval than naive top sample selection method. We also fuse BoW vector and CNN prediction in the retrieval system for a better performance. To evaluate the performance of our proposed method, experiments are conducted on Standard Oxford (5K and 105K) and Paris (6K) datasets, and experimental results and comparison with the state-of-the-art methods demonstrate the effectiveness of the proposed method.

Different types of search engines are used to search image and text contents. Two types of image search methods are available in the Internet. They are query keyword based model and content based image retrieval models. Text query strings are used in the textual image retrieval model. Content based image retrieval (CBIR) model uses the visual information of the images. Image search methods use the text annotation and image visual features. Google image search and Bing image search engines are used to fetch images from the web. Image query string is used to search image on Internet. One click query image selection method is used to submit user intention for image retrieval. Content based image re-ranking is performed with visual and textual similarity metrics. Adaptive Weight Schema is used for the similarity analysis. Feature weight learning algorithm is applied to estimate feature weights for the images and its category. Query is expanded with keyword and visual information. Rank boost framework algorithm is enhanced to rank images with photographic quality. Content similarity and visual quality factors are used for the re-ranking process. In this paper, we propose an image indexing and retrieval using speech annotations based on a predefined structured syntax. To improve the retrieval effectively, N-best lists for index generation is used .so, a query expansion technique is explored to enhance the query terms. All this process is automatic, without extra effort from the user. This is critically important in web-based image search engine for any commercial, where the user interface has to be extremely simple.

The problem of volume averaging in quantitating CSF, gray-matter, and white-matter fractions in the brain is solved using a three-compartment model and a simple graphical analysis of a multispectral MR feature space. Compartmentalization is achieved without the ambiguities of thresholding techniques or the need to assume that the underlying pixel probability distributions have a particular form. A 2D feature space is formed by double SE (proton density- and T2-weighted) MR data with image nonuniformity removed by a novel technique in which the brain itself serves as a uniformity reference. Compartments other than the basic three were rejected by the tailoring of limits in feature space. Phantom scans substantiate this approach, and the importance of the careful selection and standardization of pure tissue reference signals is demonstrated. Compartmental profiles from standardized subvolumes of three normal brains, based on a 3D (Talairach) coordinate system, demonstrate slice-by-slice detail; longitudinal studies confirm reproducibility. Compartmentalization may be described graphically and algebraically, complementing data displays in feature space and images of compartmentalized brain scans. These studies anticipate the application of our compartmentalization technique to patients with neurological disorders.

One of the main challenges in medical image retrieval is the increasing volume of image data, which render it difficult for domain experts to find relevant information from large data sets. Effective and efficient medical image retrieval systems are required to better manage medical image information. Text‐based image retrieval (TBIR) was very successful in retrieving images with textual descriptions. Several TBIR approaches rely on models based on bag‐of‐words approaches, in which the image retrieval problem turns into one of standard text‐based information retrieval; where the meanings and values of specific medical entities in the text and metadata are ignored in the image representation and retrieval process. However, we believe that TBIR should extract specific medical entities and terms and then exploit these elements to achieve better image retrieval results. Therefore, we propose a novel reranking method based on medical‐image‐dependent features. These features are manually selected by a medical expert from imaging modalities and medical terminology. First, we represent queries and images using only medical‐image‐dependent features such as image modality and image scale. Second, we exploit the defined features in a new reranking method for medical image retrieval. Our motivation is the large influence of image modality in medical image retrieval and its impact on image‐relevance scores. To evaluate our approach, we performed a series of experiments on the medical ImageCLEF data sets from 2009 to 2013. The BM25 model, a language model, and an image‐relevance feedback model are used as baselines to evaluate our approach. The experimental results show that compared to the BM25 model, the proposed model significantly enhances image retrieval performance. We also compared our approach with other state‐of‐the‐art approaches and show that our approach performs comparably to those of the top three runs in the official ImageCLEF competition.