The application of user log for online business environment using content-based Image retrieval system

Over the past few years, inter-query learning has gained much attention in the research and development of content-based image retrieval (CBIR) systems. This is largely due to the capability of inter-query approach to enable learning from the retrieval patterns of previous query sessions. However, much of the research works in this field have been focusing on analyzing image retrieval patterns stored in the database. This is not suitable for a dynamic environment such as the World Wide Web (WWW) where images are constantly added or removed. A better alternative is to use an image's visual features to capture the knowledge gained from the previous query sessions. Based on the previous work (Chung et al., 2006), the aim of this paper is to propose a framework of inter-query learning for the WWW-CBIR systems. Such framework can be extremely useful for those online companies whose core business involves providing multimedia content-based services and products to their customers

Content Based Image retrieval System

Abstract : This article describes about how technology is enhancing day by day, therefore the focus should be on new technology and new concepts which are getting implemented keeping all these things in mind the paper describes about technique for retrieving images on the basis of automaticallyderived features such as color, edge, shape -a technology now generally referred to as Content-Based Image Retrieval (CBIR). The function of our system is that a query image will be passed to cbir, also by browsing the image database folder and by selecting the image retrieval algorithm according like cedd,fcth,cld,ehd the cbir retrieves the similar images. This"Content-based" means that the search will analyze the actual contents of the image. The term 'content' in this context might refer colors, shapes, textures, or any other information that can be derived from the image itself.cbir is advantageous than purely text based image search

Feature extraction and automatic recognition of plant leaf using artificial neural network

Plant recognition is an important and challenging task. Leaf recognition plays an important role in plant recognition and its key issue lies in whether selected features are stable and have good ability to discriminate different kinds of leaves. From the view of plant leaf morphology (such as shape, dent, margin, vein and so on), domain-related visual features of plant leaf are analyzed and extracted first. On such a basis, an approach for recognizing plant leaf using artificial neural network is brought forward. The prototype system has been implemented. Experiment results prove the effectiveness and superiority of this method

Voxel-based supervised machine learning of peripheral zone prostate cancer using noncontrast multiparametric MRI

Purpose: The aim of this study was to develop and assess the performance of supervised machine learning technique to classify magnetic resonance imaging (MRI) voxels as cancerous or noncancerous using noncontrast multiparametric MRI (mp-MRI), comprised of T2-weighted imaging (T2WI), diffusion-weighted imaging (DWI), and advanced diffusion tensor imaging (DTI) parameters. Materials and methods: In this work, 191 radiomic features were extracted from mp-MRI from prostate cancer patients. A comprehensive set of support vector machine (SVM) models for T2WI and mp-MRI (T2WI + DWI, T2WI + DTI, and T2WI + DWI + DTI) were developed based on novel Bayesian parameters optimization method and validated using leave-one-patient-out approach to eliminate any possible overfitting. The diagnostic performance of each model was evaluated using the area under the receiver operating characteristic curve (AUROC). The average sensitivity, specificity, and accuracy of the models were evaluated using the test data set and the corresponding binary maps generated. Finally, the SVM plus sigmoid function of the models with the highest performance were used to produce cancer probability maps. Results: The T2WI + DWI + DTI models using the optimal feature subset achieved the best performance in prostate cancer detection, with the average AUROC, sensitivity, specificity, and accuracy of 0.93 ± 0.03, 0.85 ± 0.05, 0.82 ± 0.07, and 0.83 ± 0.04, respectively. The average diagnostic performance of T2WI + DTI models was slightly higher than T2WI + DWI models (+3.52%) using the optimal radiomic features. Conclusions: Combination of noncontrast mp-MRI (T2WI, DWI, and DTI) features with the framework of a supervised classification technique and Bayesian optimization method are able to differentiate cancer from noncancer voxels with high accuracy and without administration of contrast agent. The addition of cancer probability maps provides additional functionality for image interpretation, lesion heterogeneity evaluation, and treatment management.</p

Approche probabiliste hybride pour la recherche d'images par le contenu avec pondération des caractéristiques

Durant la dernière décennie, des quantités énormes de documents visuels (images et vidéos) sont produites chaque jour par les scientifiques, les journalistes, les amateurs, etc. Cette quantité a vite démontré la limite des systèmes de recherche d'images par mots clés, d'où la naissance du paradigme qu'on nomme Système de Recherche d'Images par le Contenu, en anglais Content-Based Image Retrieval (CBIR). Ces systèmes visent à localiser les images similaires à une requête constituée d'une ou plusieurs images, à l'aide des caractéristiques visuelles telles que la couleur, la forme et la texture. Ces caractéristiques sont dites de bas-niveau car elles ne reflètent pas la sémantique de l'image. En d'autres termes deux images sémantiquement différentes peuvent produire des caractéristiques bas-niveau similaires. Un des principaux défis de cette nouvelle vision des systèmes est l'organisation de la collection d'images pour avoir un temps de recherche acceptable. Pour faire face à ce défi, les techniques développées pour l'indexation des bases de données textuelles telles que les arbres sont massivement utilisées. Ces arbres ne sont pas adaptés aux données de grandes dimensions, comme c'est le cas des caractéristiques de bas-niveau des images. Dans ce mémoire, nous nous intéressons à ce défi. Nous introduisons une nouvelle approche probabiliste hybride pour l'organisation des collections d'images. Sur une collection d'images organisée hiérarchiquement en noeuds selon la sémantique des images, nous utilisons une approche générative pour l'estimation des mélanges de probabilités qui représentent l'apparence visuelle de chaque noeud dans la collection. Ensuite nous appliquons une approche discriminative pour l'estimation des poids des caractéristiques visuelles. L'idée dans notre travail, est de limiter la recherche seulement aux noeuds qui représentent mieux la sémantique de la requête, ce qui donne une propriété sémantique à la recherche et diminue le fossé sémantique causé par les caractéristiques de bas-niveau

Algorithm Selection for Edge Detection in Satellite Images by Neutrosophic WASPAS Method

Nowadays, integrated land management is generally governed by the principles of sustainability. Land use management usually is grounded in satellite image information. The detection and monitoring of areas of interest in satellite images is a difficult task. We propose a new methodology for the adaptive selection of edge detection algorithms using visual features of satellite images and the multi-criteria decision-making (MCDM) method. It is not trivial to select the most appropriate method for the chosen satellite images as there is no proper algorithm for all cases as it depends on many factors, like acquisition and content of the raster images, visual features of real-world images, and humans’ visual perception. The edge detection algorithms were ranked according to their suitability for the appropriate satellite images using the neutrosophic weighted aggregated sum product assessment (WASPAS) method. The results obtained using the created methodology were verified with results acquired in an alternative way—using the edge detection algorithms for specific images. This methodology facilitates the selection of a proper edge detector for the chosen image content.This article belongs to the Collection Advanced Methodologies for Sustainability Assessment: Theory and Practic

Image retrieval system based in computacional theory perceptions and fuzzy formal language

Orientador: Fernando Antônio Campos GomideDissertação (mestrado) - Universidade Estadual de Campinas, Faculdade de Engenharia Elétrica e de ComputaçãoResumo: Neste trabalho utilizam-se as teorias de Linguagem Formal Nebulosa e da Computacional das Percepções de Zadeh para definir buscas em uma base de dados gráfica. A descrição dos elementos gráficos a serem identificados é codificada por meio de sentenças aceitas por uma gramática nebulosa e definida sobre um conjunto de símbolos gráficos terminais reconhecidos por rotinas computacionais específicas. Esses símbolos terminais rotulam a imagem a ser pesquisada. A teoria da Percepção Computacional é usada para permitir que o usuário defina as relações espaciais a serem partilhadas pelos elementos gráficos na cena a ser pesquisada. Os resultados obtidos com buscas realizadas em uma base de dados gráfica com 22000 desenhos mostram que o sistema proposto fornece uma alternativa interessante para solução de buscas em bancos de dados visuaisAbstract: In this work, Fuzzy Formal Language techniques and Zadeh's Computational Theory of Perceptions are used to allow the user to query graphic data bases. The description of the graphic elements to be searched is encoded by means of fuzzy sentences accepted by a fuzzy grammar defined over a set of graphic primitives recognized by specific computational routines aimed to label different primitive graphic components of a given image. The Computational Theory of Perceptions is used to allow the user to specify the required spatial relations to be shared by the selected in the graphic scenes to be selected. The results obtained by querying a 22000 graphic scene data base support the claim that our approach provides a interesting solution for querying visual data basesMestradoEngenharia de ComputaçãoMestre em Engenharia Elétric

Automating Manufacturing Surveillance Processes Using External Observers

An automated assembly system is an integral part of various manufacturing industries as it reduces production cycle-time resulting in lower costs and a higher rate of production. The modular system design integrates main assembly workstations and parts-feeding machines to build a fully assembled product or sub-assembly of a larger product. Machine operation failure within the subsystems and errors in parts loading lead to slower production and gradual accumulation of parts. Repeated human intervention is required to manually clear jams at varying locations of the subsystems. To ensure increased operator safety and reduction in cycle-time, visual surveillance plays a critical role in providing real-time alerts of spatiotemporal parts irregularities. In this study, surveillance videos are obtained using external observers to conduct spatiotemporal object segmentation within: digital assembly, linear conveyance system, and vibratory bowl parts-feeder machine. As the datasets have different anomaly specifications and visual characteristics, we follow a bottom-up architecture for motion-based and appearance-based segmentation using computer vision techniques and deep-learning models. To perform motion-based segmentation, we evaluate deep learning-based and classical techniques to compute optical flow for real-time moving-object detection. As local and global methods assume brightness constancy and flow smoothness, results showed fewer detections in presence of illumination variance and occlusion. Therefore, we utilize RAFT for optical flow and apply its iteratively updated flow field to create a pixel-based object tracker. The tracker differentiates previous and current moving parts in different colored segments and simultaneously visualizes the flow field to illustrate movement direction and magnitude. We compare the segmentation performance of the optical flow-based tracker with a space-time graph neural network (ST-GNN), and it shows increased accuracy in boundary mask IoU alignment than the pixel-based tracker. As the ST-GNN addresses the limited dataset challenge in our application by learning visual correspondence as a contrastive random walk in palindrome sequences, we proceed with ST-GNN to perform motion-based segmentation. As ST-GNN requires a first-frame annotation mask for initialization, we explore appearance-based segmentation methods to enable automatic ST-GNN initialization. We evaluate pixel-based, interactive-based, and supervised segmentation techniques on the bowl-feeder image dataset. Results illustrate that K-means applied with watershed segmentation and gaussian blur reduces superpixel oversegmentation and generates segmentation aligned with parts boundary. Using Watershed Segmentation on the bowl-feeder image dataset, 377 parts were detected and segmented of total 476 parts present within the machine. We find that GLCM and Gabor filter perform better in segmenting dense parts regions than graph-based and entropy-based segmentation. In comparison to entropy-based and graph-based methods, the GLCM and Gabor filter segment 467 and 476 parts, respectively, of total 476 parts present within the bowl-feeder. Although manual annotation decreases efficiency, we see that the GrabCut annotation tool generates segmentation masks with increased accuracy than the pre-trained interactive tool. Using the GrabCut annotation tool, all 216 parts present within the bowl-feeder machine are segmented. To ensure segmentation of all parts within the bowl-feeder, we train Detectron2 with data augmentation. We see that supervised segmentation outperforms pixel-based and interactive-based segmentation. To address illumination variance within datasets, we apply color-based segmentation by conversion of image datasets to HSV color space. We utilize the images, converted within the value channel of HSV representation, for background subtraction techniques to detect moving bowl-feeder parts in real-time. To resolve image registration errors due to lower image resolution, we create Flex-Sim synthetic dataset with various anomaly instances consisting of multiple camera viewpoints. We apply preprocessing methods and affine-based transformation with RANSAC for robust image registration. We compare color and texture-based handcrafted features of registered images to ensure complete image alignment. We evaluate the PatchCore Anomaly detection method, pre-trained on MVTec industrial dataset, to the Flex-Sim dataset. We find that generated segmentation maps detect various anomaly instances within the Flex-Sim dataset

Techniques For Boosting The Performance In Content-based Image Retrieval Systems

Content-Based Image Retrieval has been an active research area for decades. In a CBIR system, one or more images are used as query to search for similar images. The similarity is measured on the low level features, such as color, shape, edge, texture. First, each image is processed and visual features are extract. Therefore each image becomes a point in the feature space. Then, if two images are close to each other in the feature space, they are considered similar. That is, the k nearest neighbors are considered the most similar images to the query image. In this K-Nearest Neighbor (k-NN) model, semantically similar images are assumed to be clustered together in a single neighborhood in the high-dimensional feature space. Unfortunately semantically similar images with different appearances are often clustered into distinct neighborhoods, which might scatter in the feature space. Hence, confinement of the search results to a single neighborhood is the latent reason of the low recall rate of typical nearest neighbor techniques. In this dissertation, a new image retrieval technique - the Query Decomposition (QD) model is introduced. QD facilitates retrieval of semantically similar images from multiple neighborhoods in the feature space and hence bridges the semantic gap between the images’ low-level feature and the high-level semantic meaning. In the QD model, a query may be decomposed into multiple subqueries based on the user’s relevance feedback to cover multiple image clusters which contain semantically similar images. The retrieval results are the k most similar images from multiple discontinuous relevant clusters. To apply the benifit from QD study, a mobile client-side relevance feedback study was conducted. With the proliferation of handheld devices, the demand of multimedia information retrieval on mobile devices has attracted more attention. A relevance feedback information retrieval process usually includes several rounds of query refinement. Each round incurs exchange of tens of images between the mobile device and the server. With limited wireless bandwidth, this process can incur substantial delay making the system unfriendly iii to use. The Relevance Feedback Support (RFS) structure that was designed in QD technique was adopted for Client-side Relevance Feedback (CRF). Since relevance feedback is done on client side, system response is instantaneous significantly enhancing system usability. Furthermore, since the server is not involved in relevance feedback processing, it is able to support thousands more users simultaneously. As the QD technique improves on the accuracy of CBIR systems, another study, which is called In-Memory relevance feedback is studied in this dissertation. In the study, we improved the efficiency of the CBIR systems. Current methods rely on searching the database, stored on disks, in each round of relevance feedback. This strategy incurs long delay making relevance feedback less friendly to the user, especially for very large databases. Thus, scalability is a limitation of existing solutions. The proposed in-memory relevance feedback technique substantially reduce the delay associated with feedback processing, and therefore improve system usability. A data-independent dimensionality-reduction technique is used to compress the metadata to build a small in-memory database to support relevance feedback operations with minimal disk accesses. The performance of this approach is compared with conventional relevance feedback techniques in terms of computation efficiency and retrieval accuracy. The results indicate that the new technique substantially reduces response time for user feedback while maintaining the quality of the retrieval. In the previous studies, the QD technique relies on a pre-defined Relevance Support Support structure. As the result and user experience indicated that the structure might confine the search range and affect the result. In this dissertation, a novel Multiple Direction Search framework for semi-automatic annotation propagation is studied. In this system, the user interacts with the system to provide example images and the corresponding annotations during the annotation propagation process. In each iteration, the example images are dynamically clustered and the corresponding annotations are propagated separately to each cluster: images in the local neighborhood are annotated. Furthermore, some of those images are returned to the user for further annotation. As the user marks more images, iv the annotation process goes into multiple directions in the feature space. The query movements can be treated as multiple path navigation. Each path could be further split based on the user’s input. In this manner, the system provides accurate annotation assistance to the user - images with the same semantic meaning but different visual characteristics can be handled effectively. From comprehensive experiments on Corel and U. of Washington image databases, the proposed technique shows accuracy and efficiency on annotating image databases