An automatic query expansion based on hybrid CMO-COOT algorithm for optimized information retrieval

The World Wide Web(WWW) comprises a wide range of information, and it is mainly operated on the principles of keyword matching which often reduces accurate information retrieval. Automatic query expansion is one of the primary methods for information retrieval, and it handles the vocabulary mismatch problem often faced by the information retrieval systems to retrieve an appropriate document using the keywords. This paper proposed a novel approach of hybrid COOT-based Cat and Mouse Optimization (CMO) algorithm named as hybrid COOT-CMO for the appropriate selection of optimal candidate terms in the automatic query expansion process. To improve the accuracy of the Cat and Mouse Optimization (CMO) algorithm, the parameters are tuned with the help of the Coot algorithm. The best suitable expanded query is identified from the available expanded query sets also known as candidate query pools. All feasible combinations in this candidate query pool should be obtained from the top retrieved documents. Benchmark datasets such as the GOV2 Test Collection, the Cranfield Collections, and the NTCIR Test Collection are utilized to assess the performance of the proposed hybrid COOT-CMO method for automatic query expansion. This proposed method surpasses the existing state-of-the-art techniques using many performance measures such as F-score, precision, and mean average precision (MAP).

     

An automatic image-text alignment method for large-scale web image retrieval

Multimedia Tools and Applications - For reducing huge uncertainty on the relatedness between the web images and their auxiliary text terms, an automatic image-text alignment algorithm is developed...     

An adult image identification system employing image retrieval technique

As the development of the Internet, children are easily exposed on the pornography through web browsers. To block adult images, content-based image retrieval technique is employed for adult image identification. First, the background is removed to obtain the rectangular region of interesting based on the detection of skin-like pixels. For each input image, the MPEG-7’s color, texture, and the proposed shape feature is used to retrieve 100 most similar images from the image database which contains both adult and non-adult images. If the retrieved images contains more than T_ad adult images, the input one is identified as an adult image. Otherwise, it is identified as a non-adult image. Experiment results have shown the effectiveness of the proposed method.     

An automatic image registration and overlay system

In this paper we present an automatic algorithm for registering and overlaying imagery. The algorithm basically attempts to find by successive approximations the best affine transformation or second order polynomial relating to the two images. The method requires the specification of only a matching pair of control points, then new control points are found approximately by extrapolating the old affine transformation to larger areas and then using correlation to find the best match. Thus an obvious advantage of this algorithm lies in its automatic features in locating and matching more potential ground control points. This paper also discusses the effect of the distribution of control points on the affine transformation. Finally, the method is tested on Landsat data and the results are discussed.     

An object-based image retrieval system for digital libraries

A novel approach to clustering for image segmentation and a new object-based image retrieval method are proposed. The clustering is achieved using the Fisher discriminant as an objective function. The objective function is improved by adding a spatial constraint that encourages neighboring pixels to take on the same class label. A six-dimensional feature vector is used for clustering by way of the combination of color and busyness features for each pixel. After clustering, the dominant segments in each class are chosen based on area and used to extract features for image retrieval. The color content is represented using a histogram, and Haar wavelets are used to represent the texture feature of each segment. The image retrieval is segment-based; the user can select a query segment to perform the retrieval and assign weights to the image features. The distance between two images is calculated using the distance between features of the constituent segments. Each image is ranked based on this distance with respect to the query image segment. The algorithm is applied to a pilot database of natural images and is shown to improve upon the conventional classification and retrieval methods. The proposed segmentation leads to a higher number of relevant images retrieved, 83.5% on average compared to 72.8 and 68.7% for the k-means clustering and the global retrieval methods, respectively.     

An efficient bi-layer content based image retrieval system

Multimedia Tools and Applications - Large amount of multi-media content, generated by various image capturing devices, is shared and downloaded by millions of users across the globe, every second....     

The effects of multiple query evidences on social image retrieval

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.     

DocMIR: An automatic document-based indexing system for meeting retrieval

This paper describes the DocMIR system which captures, analyzes and indexes automatically meetings, conferences, lectures, etc. by taking advantage of the documents projected (e.g. slideshows, budget tables, figures, etc.) during the events. For instance, the system can automatically apply the above-mentioned procedures to a lecture and automatically index the event according to the presented slides and their contents. For indexing, the system requires neither specific software installed on the presenter’s computer nor any conscious intervention of the speaker throughout the presentation. The only material required by the system is the electronic presentation file of the speaker. Even if not provided, the system would temporally segment the presentation and offer a simple storyboard-like browsing interface. The system runs on several capture boxes connected to cameras and microphones that records events, synchronously. Once the recording is over, indexing is automatically performed by analyzing the content of the captured video containing projected documents and detects the scene changes, identifies the documents, computes their duration and extracts their textual content. Each of the captured images is identified from a repository containing all original electronic documents, captured audio–visual data and metadata created during post-production. The identification is based on documents’ signatures, which hierarchically structure features from both layout structure and color distributions of the document images. Video segments are finally enriched with textual content of the identified original documents, which further facilitate the query and retrieval without using OCR. The signature-based indexing method proposed in this article is robust and works with low-resolution images and can be applied to several other applications including real-time document recognition, multimedia IR and augmented reality systems.

Supporting image retrieval framework with rule base system

The present paper introduces an image retrieval framework based on a rule base system. The proposed framework makes use of color and texture features, respectively called color co-occurrence matrix (CCM) and difference between pixels of scan pattern (DBPSP). These features are used to perform the image mining for acquiring clustering knowledge from a large empirical images database. Irrelevance between images of the same cluster is precisely considered in the proposed framework through a relevance feedback phase followed by a novel clustering refinement model. The images and their corresponding classes pass to a rule base system for extracting a set of accurate rules. These rules are pruning and may reduce the dimensionality of the extracted features. The advantage of the proposed framework is reflected in the retrieval process, which is limited to the images in the class of rule matched with the query image features. Experiments show that the proposed model achieves a very good performance in terms of the average precision, recall and retrieval time compared with other models.     

Deep-MATEM: TEM query image based cross-modal retrieval for material science literature

With the rapid increasing of published material science literatures, an effective literature retrieving system is important for researchers to obtain relevant information. In this paper we propose a cross-modal material science literatures retrieval method using transmission electron microscopy(TEM) image as query information, which provide a access of using material experiment generated TEM image data to retrieve literatures. In this method, terminologies are extracted and topic distribution are inferred from text part of literatures by using LDA, and we design a multi-task Convolutional Neuron Network(CNN) mapping query TEM image to the relevant terminologies and topic distribution predictions. The ranking score is calculated from output for query image and text data. Experimental results shows our method achieves better performance than multi-label CCA, Deep Semantic Matching(Deep SM) and Modality-Specific Deep Structure(MSDS).     

An adaptive neuro-fuzzy system for automatic image segmentation andedge detection

An autoadaptive neuro-fuzzy segmentation and edge detection architecture is presented. The system consists of a multilayer perceptron (MLP)-like network that performs image segmentation by adaptive thresholding of the input image using labels automatically pre-selected by a fuzzy clustering technique. The proposed architecture is feedforward, but unlike the conventional MLP the learning is unsupervised. The output status of the network is described as a fuzzy set. Fuzzy entropy is used as a measure of the error of the segmentation system as well as a criterion for determining potential edge pixels. The proposed system is capable to perform automatic multilevel segmentation of images, based solely on information contained by the image itself. No a priori assumptions whatsoever are made about the image (type, features, contents, stochastic model, etc.). Such an "universal" algorithm is most useful for applications that are supposed to work with different (and possibly initially unknown) types of images. The proposed system can be readily employed, "as is," or as a basic building block by a more sophisticated and/or application-specific image segmentation algorithm. By monitoring the fuzzy entropy relaxation process, the system is able to detect edge pixels     

FReBIR: An image retrieval system based on fuzzy region matching

In this paper, we present a method of image indexing and retrieval which takes into account the relative positions of the regions within the image. Indexing is based on a segmentation of the image into fuzzy regions; we propose an algorithm which produces a fuzzy segmentation. The image retrieval is based on inexact graph matching, taking into account both the similarity between regions and the spatial relation between them. We propose, on one hand a solution to reduce the combinatorial complexity of the graph matching, and on the other hand, a measure of similarity between graphs allowing the result images ranking. A relevance feedback process based on region classifiers allows then a good generalization to a large variety of the regions. The method is adapted to partial queries, aiming for example at retrieving images containing a specific type of object. Applications may be of two types, firstly an on-line search from a partial query, with a relevance feedback aiming at interactively leading the search, and secondly an off-line learning of categories from a set of examples of the object. The name of the system is FReBIR for Fuzzy Region-Based Image Retrieval.     

图像信息检索系统的设计

对采用基于图像频率特征匹配和图像识别方法来实现图像信息检索进行了研究.先用小波分析作为特征提起方法,由于小波分析具有多分辨率分析的特点,并且在提取图像频域特征的同时,保留了图像的空间信息,因此用它来提取图像特征;而后根据计算量的大小,分别用改进的二维相关性算法和二维动态规划弹性算法进行图像的匹配和识别;用VC 和Matlab混合编程进行系统构建并进行了测试实验.实验结果表明,系统有良好的检索效果.     

Multi-panel medical image segmentation framework for image retrieval system

The automatic segmentation of multi-panel medical images into sub-images improves the retrieval accuracy of medical image retrieval systems. However, the accuracy and efficiency of the available multi-panel medical image segmentation techniques are not satisfactory for multi-panel images containing homogenous color inter-panel borders and image boundary, heterogeneous color inter-panel borders, small size sub-images, or numerous number of sub-images. In order to improve the accuracy and efficiency, a Multi-panel Medical Image Segmentation Framework (MIS-Framework) is proposed and implemented based on locating the longest inter-panel border inside the boundary of the input image. We evaluated the proposed framework on a subset of imageCLEF 2013 dataset containing 2407 images. The proposed framework showed promising experimental results in terms of accuracy and efficiency on single panel as well as multi-panel image class identification and on sub-image separation as compared to the available techniques.     

Conceptual query formulation and retrieval

In this paper, we advance a technique to develop a user profile for information retrieval through knowledge acquisition techniques. The profile bridges the discrepancy between user-expressed keywords and system-recognizable index terms. The approach presented in this paper is based on the application of personal construct theory to determine a user's vocabulary and his/her view of different documents in a training set. The elicited knowledge is used to develop a model for each phrase/concept given by the user by employing machine learning techniques.Our model correlates the concepts in a user's vocabulary to the index terms present in the documents in the training set. Computation of dependence between the user phrases also contributes in the development of the user profile and in creating a classification of documents. The resulting system is capable of automatically identifying the user concepts and query translation to index terms computed by the conventional indexing process. The system is evaluated by using the standard measures of precision and recall by comparing its performance against the performance of the smart system for different queries.This research is supported by the NSF grant IRI-8805875.     

Interactive and automatic image recognition system

We have developed a vision system which learns to recognize many kinds of two-dimensional objects in many kinds of images. Image processing program modules are classified based on functions in the library. First, the user can teach the system the way to recognize objects in the image interactively testing the effectiveness of each program by trial and error. The system stores what it learns in the long-term memory calledmodel. The model is improved by analyzing training images in the same way. Once the model is completed, the system can automatically analyze images in the same category and recognize the expected objects in a top-down way driven by the model. Since a model is built for images in each category, the system can recognize various kinds of images simply by retrieving the corresponding models.     

An adaptive technique for content-based image retrieval

We discuss an adaptive approach towards Content-Based Image Retrieval. It is based on the Ostensive Model of developing information needs—a special kind of relevance feedback model that learns from implicit user feedback and adds a temporal notion to relevance. The ostensive approach supports content-assisted browsing through visualising the interaction by adding user-selected images to a browsing path, which ends with a set of system recommendations. The suggestions are based on an adaptive query learning scheme, in which the query is learnt from previously selected images. Our approach is an adaptation of the original Ostensive Model based on textual features only, to include content-based features to characterise images. In the proposed scheme textual and colour features are combined using the Dempster-Shafer theory of evidence combination. Results from a user-centred, work-task oriented evaluation show that the ostensive interface is preferred over a traditional interface with manual query facilities. This is due to its ability to adapt to the user's need, its intuitiveness and the fluid way in which it operates. Studying and comparing the nature of the underlying information need, it emerges that our approach elicits changes in the user's need based on the interaction, and is successful in adapting the retrieval to match the changes. In addition, a preliminary study of the retrieval performance of the ostensive relevance feedback scheme shows that it can outperform a standard relevance feedback strategy in terms of image recall in category search.