Relevance Feedback Mechanism Research Articles

Optimization Technique Based Approach for Image Segmentation.

The study's goal was to diagnose the condition at an earlier stage by employing the optimization-based technique for image segmentation to find deformities in MRI and Aura images Methods: Our methodology was based on two case studies. The diseased data set of MRI images obtained from the UCI data set and Aura images from Bio-Well were taken into consideration. Using the Relevance Feedback Mechanism (RFM), the sick images that are most pertinent are determined. The optimization-based Cuckoo Search (CS) algorithm is used to find the best features. The resulting model utilising the Truncated Gaussian Mixture Model (TGMM) is used to compare the extracted characteristics. The most relevant images are chosen based on the likely hood estimation. The suggested methodology is tested using 150 retrieved Aura images, 50 trained photos, and processing of the input image utilizing morphological techniques like dilation, erosion, opening, and closing to improve the image quality. Together with segmentation quality measurements including Global Consistency Error (GCE), Probability Random Index (PRI), and Volume of Symmetry(VOS), the results are assessed using image quality metrics such as Average Difference (AD), Maximum Difference (MD), and Image Fidelity (IF). The TGMM algorithm is used to conduct the experiment. The outcomes demonstrate the effectiveness of the suggested approaches in locating various injured tissues inside medical images obtained using MRI technology as well as in locating high-intensity energy zones in which a potential deformity is associated in Aura images. The outcomes reveal a respectable recognition accuracy of about 93%.

A New Unified Theory of Trigger Point Formation: Failure of Pre- and Post-Synaptic Feedback Control Mechanisms.

The origin of the myofascial trigger point (TrP), an anomalous locus in muscle, has never been well-described. A new trigger point hypothesis (the new hypothesis) presented here addresses this lack. The new hypothesis is based on the concept that existing myoprotective feedback mechanisms that respond to muscle overactivity, low levels of adenosine triphosphate, (ATP) or a low pH, fail to protect muscle in certain circumstances, such as intense muscle activity, resulting in an abnormal accumulation of intracellular Ca2+, persistent actin-myosin cross bridging, and then activation of the nociceptive system, resulting in the formation of a trigger point. The relevant protective feedback mechanisms include pre- and postsynaptic sympathetic nervous system modulation, modulators of acetylcholine release at the neuromuscular junction, and mutations/variants or post-translational functional alterations in either of two ion channelopathies, the ryanodine receptor and the potassium-ATP ion channel, both of which exist in multiple mutation states that up- or downregulate ion channel function. The concepts that are central to the origin of at least some TrPs are the failure of protective feedback mechanisms and/or of certain ion channelopathies that are new concepts in relation to myofascial trigger points.

Construction of artificial intelligence-assisted English learning resource query system.

English has become an important tool for China's opening to the outside world and exchanges with other countries. More and more people have the motivation and requirements to learn English, but under the traditional English learning mode and traditional teaching mode, the cultivation of learners' autonomous learning habits is ignored. This article aims to study the construction of artificial intelligence-assisted English learning resource query system and establish the relevant feedback mechanism of retrieval. This article applies this mechanism to the retrieval of learning resources, so as to provide learners with the learning resources they really need and improve learners' learning efficiency. This article proposes to find the relevant knowledge points by extracting the knowledge points of the retrieval content. It realizes the query expansion based on knowledge and then realizes the expansion of retrieval results. It realizes the mapping of knowledge points on the retrieval content, the query and expansion of knowledge points, and the presentation of learning resources of the knowledge point index. It also uses the relevant feedback mechanism to adjust the retrieval results to meet the retrieval needs of learners. The experimental results show that the number of knowledge points can be increased to 2–4 times by query expansion based on English resources. Thus, the number of learning resources of search results can be increased to 3–10 times, the expansion of search results can be realized, and the overall recall will be greatly improved. In this article, the related methods of artificial intelligence are applied to the construction experiment of the English learning resource query system, which has a certain promotion effect on the construction of the system.

What adds more flexibility? An energy system analysis of storage, demand-side response, heating electrification, and distribution reinforcement

This paper compares various flexibility options to support renewable energy integration across the energy transition using energy system modelling. We analyse new flexibility assets such as electricity storage, heat pumps, demand-side response with existing wet appliances, electric boilers for domestic hot water and distribution grid expansion, along with energy efficiency measures in electrical appliances and building retrofitting. We propose an open-source sector coupling model (GRIMSEL-FLEX) to minimise, from a social planner perspective, the total cost of the energy system for electricity and residential heating supply in Switzerland, including various types of consumers and urban settings. We find relevant feedback mechanisms among various flexibility options. Firstly, electric boilers have a larger flexibility potential than demand-side response with wet appliances since they reduce storage investments by more than 26% by 2050 (only 12% for demand-side response). Secondly, 34% more electricity storage is needed if heat pumps replace all fossil-based heating and 80% to replace all heating systems entirely. Thirdly, we find a shift in the operation of heat pumps, electric boilers and wet appliances from night to midday, resulting in larger photovoltaic deployment (22%–66% for the residential sector). Finally, electricity storage capacity induced by heat pump deployment is highly dependent on the retrofitting rate. With 1% per annum, 86% of storage investments can be avoided and it can be counterbalanced with a high retrofitting rate of 2% per annum.

Iterative brain tumor retrieval for MR images based on user’s intention model

Generally, medical content-based image retrieval (CBIR) systems select low-level visual features as image descriptors. However, these descriptors fail to provide clues for understanding the content of medical images in a similar way as a human expert, which makes the retrieval results inconsistent with the user’s intention. To solve this problem, we propose a closed-loop brain tumor retrieval system for MR images with an eye-tracking based relevance feedback mechanism. In our method, we first model the intention of the user by training a convolutional neural network based on the temporal and spatial features extracted from his/her eye-tracking data collected when inspecting the relevance between different images. Upon using visual features as a bridge, the relevancy degree to the query image of any of the database images is computed with our user’s intention model by transferring to it the eye movement data from the most visually similar image amongst images iteratively accumulated in the canvas. Our proposed retrieval system is implemented in an iterative manner. In each round of iteration, user’s eye movement data when inspecting the system returns are collected and the canvas collection of images is also updated by appending to it the user inspected system returns. With the updated canvas collections, the relevancy degree of database images can be recomputed and the system can begin a new round search of the most relevant images. Extensive experiments have been performed on a publicly available T1-weighted contrast-enhanced magnetic resonance image (CE-MRI) dataset that consists of three types of brain tumors (glioma, meningioma, and pituitary tumor) collected from 233 patients with a total of 3064 images across the axial, coronal, and sagittal views. Experimental results of 22 volunteers (11 males and 11 females, with an average age of 24.4 years) from our medical school show that upon implicit involvement of users in the brain tumor retrieving process, our proposed system significantly outperforms state-of-the-art methods and achieves Prec@10 to 99.94%, mAP to 97.95% after the third round of iteration.

A two-stage information retrieval system based on interactive multimodal genetic algorithm for query weight optimization

Query weight optimization, which aims to find an optimal combination of the weights of query terms for sorting relevant documents, is an important topic in the information retrieval system. Due to the huge search space, the query optimization problem is intractable, and evolutionary algorithms have become one popular approach. But as the size of the database grows, traditional retrieval approaches may return a lot of results, which leads to low efficiency and poor practicality. To solve this problem, this paper proposes a two-stage information retrieval system based on an interactive multimodal genetic algorithm (IMGA) for a query weight optimization system. The proposed IMGA has two stages: quantity control and quality optimization. In the quantity control stage, a multimodal genetic algorithm with the aid of the niching method selects multiple promising combinations of query terms simultaneously by which the numbers of retrieved documents are controlled in an appropriate range. In the quality optimization stage, an interactive genetic algorithm is designed to find the optimal query weights so that the most user-friendly document retrieval sequence can be yielded. Users’ feedback information will accelerate the optimization process, and a genetic algorithm (GA) performs interactively with the action of relevance feedback mechanism. Replacing user evaluation, a mathematical model is built to evaluate the fitness values of individuals. In the proposed two-stage method, not only the number of returned results can be controlled, but also the quality and accuracy of retrieval can be improved. The proposed method is run on the database which with more than 2000 documents. The experimental results show that our proposed method outperforms several state-of-the-art query weight optimization approaches in terms of the precision rate and the recall rate.

Remote Sensing Image Retrieval using Hybrid Visual Geometry Group Network with Relevance Feedback

ABSTRACT The advancement in the field of remote sensing (RS) has offered a vast number of RS images with higher resolution. Nowadays, remote sensing image retrieval (RSIR) has become a challenging task for researchers due to the complicated contents and specific characteristics of RS images. Content-Based Image Retrieval (CBIR) methods create powerful tools for mining large RS image databases. Content-based RSIR aims to acquire the images with similar visual content based on a query given from a large-scale RS image library. Most of the previous works used pre-trained convolutional neural network (CNN) to form a scene illustration for the classification of RS scenes. In this work, an effective RSIR using hybrid VGGNet (Visual Geometry Group Network) CNN with red deer algorithm (RDA) is presented for the appropriate retrieval of RS images based on the query image. The proposed hybrid VGGNet CNN model integrates dimensionality reduction (DR), feature extraction (FE), loss function optimization, matching process and relevance feedback (RF) mechanism. The technique used to reduce the dimensionality is Principal Component Analysis (PCA). Next, feature extraction is processed using the combination of hybrid VGGNet CNN model, attention module and convolutional feature encoding module. The loss function is optimized using RDA, and the matching process is done by recursive density estimation (RDE). Finally, based on the feedback obtained from the user, the RF mechanism discards the images that are non-relevant and only relevant images are retrieved. The tool used for implementation is PYTHON platform. Hence, the extensive tests on University of California Merced (UCM) and Wuhan University Remote Sensing images with 19 class (WHU-RS19) databases reveal that the proposed model performs better than several state-of-the-art techniques.

Convolutional neural networks for relevance feedback in content based image retrieval

Given the great success of Convolutional Neural Network (CNN) for image representation and classification tasks, we argue that Content-Based Image Retrieval (CBIR) systems could also leverage on CNN capabilities, mainly when Relevance Feedback (RF) mechanisms are employed. On the one hand, to improve the performances of CBIRs, that are strictly related to the effectiveness of the descriptors used to represent an image, as they aim at providing the user with images similar to an initial query image. On the other hand, to reduce the semantic gap between the similarity perceived by the user and the similarity computed by the machine, by exploiting an RF mechanism where the user labels the returned images as being relevant or not concerning her interests. Consequently, in this work, we propose a CBIR system based on transfer learning from a CNN trained on a vast image database, thus exploiting the generic image representation that it has already learned. Then, the pre-trained CNN is also fine-tuned exploiting the RF supplied by the user to reduce the semantic gap. In particular, after the user’s feedback, we propose to tune and then re-train the CNN according to the labelled set of relevant and non-relevant images. Then, we suggest different strategies to exploit the updated CNN for returning a novel set of images that are expected to be relevant to the user’s needs. Experimental results on different data sets show the effectiveness of the proposed mechanisms in improving the representation power of the CNN with respect to the user concept of image similarity. Moreover, the pros and cons of the different approaches can be clearly pointed out, thus providing clear guidelines for the implementation in production environments.

A Mechanism for Sketch Based Image Retrievals using Generalized Gamma Mixture Model (GGMM) and Relevance Feedback Mechanism

The exponential growth of multimedia technologies facilitated the ease of developing various images having different shapes and scales. With the advent of mobile technologies, these messages so generated are being transmitted across the globe in different formats for different purposes. With these advancements methodologies thus developed for identifying or expressing subjects (individuals) their views by means of sketches. These sketch based images have many advantages, in particular, these images can be well considered in situations where the narration and capturing becomes difficult. The present article underlines a mechanism to interpret the images and also addresses the retrieval of such sketch based images using Generalized Gamma Mixture Model. The relevance feedback mechanism is utilized to retrieve more relevant to sketch based images based on the query image. The efficiency of the proposed word is evaluated using metrics like precision, recall, error rate, and retrieval accuracy.

Man in the Middle of Face Recognition System: using Skin Color and Template

During last 10 years people are very much attracted to face recognition systems and they are very much eager to solve the issues related to face recognition system. It helped them very much in the field of electronics and uses over pattern unlocking and password entering system. There are numerous applications as for security, affectability and mystery. Detection of a face is the most significant and initial step of recognition framework. This article demonstrates a new method to face recognition system using color and template of an image. Whatever the background it may go to be, our system will detect the face, which is an important stage for face detection. The pictures utilized in this framework for Face detection are the color images, while the images used for the Face Recognition are the Gray images which are converted from color pictures. The illumination compensation technique is applied on all the images for removing the effect of light. The Red, Green, and Blue values of each pixel will be converted to YCbCr space. Based on the probability of each pixel in terms of Cb, Cr values, we extract the skin pixels from the query image,. The positive probability shows a “skin pixel”, while the negative probability shows “not a skin pixel”. Finally the face is projected. In face recognition, we used 4 templates of different sizes for Gabor image content extraction. Finally we employed the relevance feedback mechanism to retrieve the most similar images. If the user did not satisfy with the given results he can give the correct images to the system from the displayed images. Exploratory outcomes demonstrate that the demonstrated system is adequate to recognize face of a human face in a picture with an exactness of 94%.

Medical social network content analysis for medical image retrieval purpose

SummaryBackground and objective: Medical social networking platforms provide virtual spaces ensuring the interaction between different healthcare participants. As a part of the exchange, these spaces allow subscribers to upload medical images, describing different medical cases for an analysis or an interpretation proposal. Facing this expected huge amount of uploaded images generated daily, it is needed to engage new mechanisms to effectively deal with this circumstance, for enhancing the search function process of medical images, based on what is uploaded. To overcome this issue, setting up of images visual searching based on a content‐based medical image retrieval scheme is the solution. More clearly, such mechanism will help and motivate medical social networking subscribers to find visually similar stored images.Methods: To ensure this task, the development of this mechanism, technically, is based mainly on a fusion of three visual features, which offers a flexible and more precision. It is reinforced by a weighted distance approach through attributing weights for feature vectors to scale up the performance. Indeed, the displayed results of this system can be updated based on user's intention by a user interactive feedback mechanism to indicate the truly relevant images.Results: We provide the theoretical performance of our scheme. Extensive experiments were conducted on a categorically classified collection containing 500 images. We conduct a practical evaluation on this dataset classes, putting returned results in a comparative study with other models results, existing in the literature.Conclusions: The proposed scheme preserves the efficiency of the search task. As theoretically and experimentally established, our scheme offers an effective image retrieval model that can support different subscribers' expectations. The relevance feedback mechanism can keep the dynamism of the system, thus offering a continuous searching result evolution. Experimentation outcomes indicate better findings compared with the other models.

Deep CNN Combined With Relevance Feedback for Trademark Image Retrieval

Abstract Trademark recognition and retrieval is a vital appliance component of content-based image retrieval (CBIR). Reduction in the semantic gap, attaining more accuracy, reduction in computation complexity, and hence in execution time, are the major challenges in designing and developing a trademark retrieval system. The direction of the proposed work takes into account these challenges by implementing trademark image retrieval through deep convolutional neural networks (DCNNs) integrated with a relevant feedback mechanism. The dataset features are optimized through particle swarm optimization (PSO), reducing the search space. These best/optimized features are given to the self-organizing map (SOM) for clustering at the preprocessing stage. The CNN model is trained on feature representations of relevant and irrelevant images, using the feedback information from the user bringing the marked relevant images closer to the query. Experimentation proved a significant performance when evaluated using FlickrLogos-27, FlickrLogos-32, and FlickrLogos-32 PLUS datasets, as illustrated in the performance results section.

Secure Certificateless Authentication and Road Message Dissemination Protocol in VANETs

As a crucial component of Internet‐of‐Thing (IoT), vehicular ad hoc networks (VANETs) have attracted increasing attentions from both academia and industry fields in recent years. With the extensive VANETs deployment in transportation systems of more and more countries, drivers’ driving experience can be drastically improved. In this case, the real‐time road information needs to be disseminated to the correlated vehicles. However, due to inherent wireless communicating characteristics of VANETs, authentication and group key management strategies are indispensable for security assurance. Furthermore, effective road message dissemination mechanism is of significance. In this paper, we address the above problems by developing a certificateless authentication and road message dissemination protocol. In our design, certificateless signature and the relevant feedback mechanism are adopted for authentication and group key distribution. Subsequently, message evaluating and ranking strategy is introduced. Security analysis shows that our protocol achieves desirable security properties. Additionally, performance analysis demonstrates that the proposed protocol is efficient compared with the state of the art.

Modified one-class support vector machine for content-based image retrieval with relevance feedback

AbstractImage retrieval via traditional Content-Based Image Retrieval (CBIR) often incurs the semantic gap problem—non-correlation of image retrieval results with human semantic interpretation of images. In this paper, Relevance Feedback (RF) mechanism was incorporated into a traditional Query by Visual Example CBIR (QVER) system. The inherent curse of dimensionality associated with RF mechanism was catered for by performing feature selection using Principal Component Analysis (PCA). The amount of feature dimension retained was determined based on a not more than 5% loss constrain imposed on average precision of retrieval result. While the asymmetry and small sample size nature of the resultant image dataset informed the use of a modified One-Class Support Vector Machine (OC-SVM) classifier, three image databases (DB10, DB20 and DB100) were used to test the OC-SVM RF mechanism. Across DB10, DB20 and DB100, Average Indexing Time of 0.451, 0.3017, and 0.0904s were recorded, respectively. For a critical reca...

Novel System for Color Logo Recognition Using Optimization and Learning Based Relevance Feedback Technique

Logo recognition system deals with matching of the input trademark or logo with stored trademark images in database. This application, under CBIR umbrella, focuses on optimizing search through database by extracting minimum features from set of the images and using relevance feedback mechanism to identify the relevant images. Obtaining higher accuracy in retrieval process is the main challenge of the work. The retrieval results of CBIR system can be enhanced by using machine learning mechanisms with relevance feedback for Short Term Learning (STL) and Long-Term Learning (LTL). This paper proposes the relevance feedback system embedded with machine learning and optimization technique for logo recognition. Relevance feedback technique is used as baseline model for logo recognition. Feature set is optimized using particle swarm optimization (PSO) and search process is made intelligent by incorporating self-organizing map (SOM). These techniques improve the basic model as depicted in the results.

Personal Web Revisitation by Context and Content Keywords with Relevance Feedback

Getting back to previously viewed web pages is a common yet uneasy task for users due to the large volume of personally accessed information on the web. This paper leverages human's natural recall process of using episodic and semantic memory cues to facilitate recall, and presents a personal web revisitation technique called WebPagePrev through context and content keywords. Underlying techniques for context and content memories’ acquisition, storage, decay, and utilization for page re-finding are discussed. A relevance feedback mechanism is also involved to tailor to individual's memory strength and revisitation habits. Our 6-month user study shows that: (1) Compared with the existing web revisitation tool Memento , History List Searching method, and Search Engine method, the proposed WebPagePrev delivers the best re-finding quality in finding rate (92.10 percent), average F1-measure (0.4318), and average rank error (0.3145). (2) Our dynamic management of context and content memories including decay and reinforcement strategy can mimic users’ retrieval and recall mechanism. With relevance feedback, the finding rate of WebPagePrev increases by 9.82 percent, average F1-measure increases by 47.09 percent, and average rank error decreases by 19.44 percent compared to stable memory management strategy. Among time, location, and activity context factors in WebPagePrev , activity is the best recall cue, and context+content based re-finding delivers the best performance, compared to context based re-finding and content based re-finding.

Financial stability at risk due to investing rapidly in renewable energy

We present novel insights about effective energy policies using an agent-based model. The model describes relevant feedback mechanisms between technological evolution, the interbank market and the electricity sector. Analysis with it shows that energy policies affect interbank connectivity and hence the likelihood of cascades of bank failures. This effect has not been studied before in the literature. In particular, we find that investments in renewable energy reduce interbank connectivity, increasing the probability of bank failures, while raising taxes on energy has an opposite effect. Increasing the share of renewable energy in electricity production initially increases the price of electricity, and thus improves profits and the ability to re-pay debts of incumbent power plants. However, when the share of renewable energy increases too quickly, financial stability may be at stake as the burden of financing investments in renewable energy offsets the improved profitability of existing power stations. All in all, this study provides a unique and novel perspective on the relationship between renewable energy investments and financial stability.

An image texture insensitive method for saliency detection

We propose a texture insensitive, region based image saliency detection algorithm, having excellent detection and localization properties, to obtain salient objects. We use a total variation based regularizer to suppress textures from the image and to make the method invariant to textural variations in the scene. This leads to an image that contains piecewise constant gray valued regions. This texture-free image is sparsely segmented into a small number of regions using the expectation maximization algorithm assuming a Gaussian mixture model. We compute three different saliency measures for every region using its intensity and spatial features. We adopt a relevance feedback mechanism to obtain weights for combining the three saliency measures and obtain the final saliency map. Next we input the thresholded saliency map to an image matting technique and extract the salient objects from the image with exact boundaries. Experimental comparisons with existing saliency detection algorithms demonstrate the superiority of the proposed technique.

Heterogeneous Manifold Ranking for Image Retrieval

Graph-based ranking models, such as manifold ranking (MR), have been widely used in various image retrieval applications. To further improve such models, a current trend is to fuse the ranking results from multiple feature sets. Most of existing methods mainly concentrate on fusing the homogeneous feature sets derived from a single information channel, like the multiple modalities of image visual content, but little is known in fusing such heterogeneous feature sets derived from multiple information channels as the click-through data associated with images and their visual content. The primary challenge is how to effectively exploit the complementary properties of the heterogeneous feature sets. Another tough issue is the low-quality nature of the click-through data, which makes the exploration of such complementary properties more difficult. In this paper, we propose a heterogeneous MR (HMR) model, in which a couple of graphs built on the click and visual feature sets are fused to simultaneously encode the image ranking results. Specifically, our HMR model applies different solutions to fuse the heterogeneous feature sets in terms of whether the relevance feedback mechanism is available or not. In addition, we develop a click refinement technique to address the noiseness and sparseness problems inherent in the click-through data. Concretely, it prunes the inaccurate clicks from the click-through data using a neighbor voting strategy, and then enriches the pruned data with novel yet accurate clicks based on a novel collaborative filtering (CF) approach, which is devised by integrating the merits of three popularly used CF methods, thus called Tri-CF algorithm. Extensive experiments on the tasks of click refinement and image retrieval demonstrate the superior performance of the proposed algorithms over several representative methods, especially when the click-through data is highly noisy and sparse.

Pseudo relevance feedback based on majority voting mechanism

The pseudo relevance feedback mechanism has come to improve the performance of the CBIR systems before visualising the final results and without any user assistance. In this paper, we show the superiority of our proposed a pseudo relevance feedback scheme 'majority voting algorithm'. The algorithm is compared to other approaches of the literature of that clustering materialised on two well known clustering algorithms namely: hierarchical agglomerative clustering method (HACM) and K-means and pseudo query reformulation materialised on pseudo query point movement, pseudo standard Rocchio formula and pseudo adaptive shifting query. Experiments are conducted on the heterogeneous Wang (COREL-1K) database and Google image engine using the colour moments as a signature. This work enables us to compare some pseudo relevance feedback techniques of the literature while the obtained results show the clear superiority of our proposed algorithm.