Image Retrieval

ABSTRACT A major disadvantage of image retrieval systems is their lack of objects orientation and scaling matching. This paper addresses the issue of accurate, effective, computationally efficient, fast and fully-automated 2D object orientation and scaling determination. The approach relies on the objects frequency-based features. The frequency-based features used by the proposed technique, are extracted by a 2D physics-based deformable model that parameterizes the objects shape. The method was evaluated on synthetic and real images. The experimental results demonstrate the accuracy of the method, both in orientation and the scaling estimations

The advancement in medical imaging has resulted in a rapid and large increase in medical images inside repositories. These medical images contain very important information that can be used in many things, including diagnosing diseases. This implies that a precise, efficient way of indexing and retrieving biomedical images is necessary to obtain medical images from such repositories in real-time. CBMIR, therefore, played an important part, where the CBMIR’s area is very important in the field of image processing and involves low-level feature extraction and similarity measures for the comparison of medical images such as color histograms, edges, texture, shape. The majority of the methods already in use in CBMIR enhance the retrieval of a medical image and diseases diagnosis by reducing the issue of the semantic gap between low visual and high semantic levels. Also, secure access to the medical image of diverse cases, which are often kept on a network and are susceptible to maliciou...

The main objective in content-based image retrieval is to find images similar to a query image in an image collection. Matching using descriptors computed from regions centered at local invariant interest points (key points) have become popular because of their robustness to changes in viewpoint and occlusion. However, local descriptor matching can produce many false matches. RANSAC can robustly fit a model to data in presence of outliers and has been used to find correspondences in presence of noise. But obtaining a good hypothesis may require many runs, particularly when the proportion of inliers in the data is low. In this paper, we utilize topological information from the Delaunay triangulation to construct a refined set of matches that is presented to the RANSAC algorithm to fit a homography. Experiments show that with this refined match list, RANSAC is able to obtain correct hypothesis in very few runs. The result is often superior to ordinary RANSAC even after thousands of runs and the method consumes substantially less processing time.

International Journal of VLSI design & Communication Systems (VLSICS) is a bi monthly open access peer-reviewed journal that publishes articles which contribute new results in all areas of VLSI Design & Communications. The goal of this journal is to bring together researchers and practitioners from academia and industry to focus on advanced VLSI Design & communication concepts and establishing new collaborations in these areas.

Authors are solicited to contribute to this journal by submitting articles that illustrate research results, projects, surveying works and industrial experiences that describe significant advances in the VLSI design & Communications.

ABSTRACT In recent years, there are available extremely large collections of images located on distributed and heterogeneous platforms over the online web service. The proliferation of digital cameras and the growing photo sharing using current technology for browsing such collections, but at the same time it spurred the emergence of new image retrieval techniques based not only on photos' visual information, but on geo-location tags. Currently image retrieval systems; the retrieval process is performed using similarity strategies applied on certain features in the image. In this paper, we proposed a process of image refining retrieval result by exploiting and fusing unsupervised feature technique Principal component analysis (PCA) and spectral clustering. PCA algorithm is used for to remove the outliers from the initially retrieved image set, and then it uses Principal Component Analysis (PCA) to extract principal components of the feature values. Later on, feature values of each image are exhibited by a linear combination of these principal components. Spectral clustering analyzes retrieval process by clustering together visually similar images.PCA and spectral clustering require manual turning of their parameters, which usually requires a priori knowledge of the dataset. To overcome this problem we developed a tuning mechanism that automatically tunes the parameters of both algorithms. For the evaluation of the proposed approach we used thousands of images from Flickr downloaded using text queries for well-known cultural heritage monuments. The proposed method was performed and tested on a set of images from variant sceneries. Experimental results show the superior performance of this approach.

World dynamic situation are full of random changes which include multiple factors like audio, video and image. So cause of concurrent incident and simultaneously device performance requirement become typical for information retrieval. That make segmentation approach a method of combining feature transformation with clustering algorithm which is proposed for adequate retrieval of image, that could take input approach equally as similar platform and after function performance of analyses and processing that methodology make image, audio, video information separate and retrieve them as much clear as possible then give a best clear resultant as per certainty.

Arrangement of description of any specific object have a pattern structure in image processing filed to analyze and observe a targeted object and declare as goal is a hot field of research. For a random situation complexity is going to increase when we have classes that specifies concurrently and co-relate with each other with complex database. With an algorithm to follow sketch and finalized as a particular object point relation to achieve the target is my approach in this paper. To sense an observation with CBIR, a neural network phenomenon also used here.

Automatic image annotation is the task of automatically assigning some form of semantic label to images, such as words, phrases or sentences describing the objects, attributes, actions, and scenes depicted in the image. In this chapter, we present an overview of the various automatic image annotation tasks that were organized in conjunction with the ImageCLEF track at CLEF between 2009–2016. Throughout the 8 years, the image annotation tasks have evolved from annotating Flickr photos by learning from clean data to annotating web images by learning from large-scale noisy web data. The tasks are divided into three distinct phases, and this chapter will provide a discussion for each of these phases. We will also compare and contrast other related benchmarking challenges, and provide some insights into the future of automatic image annotation.

Three-dimensional models are more and more used in applications in which the necessity to visualize realistic objects is felt (CAD/CAO, medical simulations, games, virtual reality etc.). Consequently, the management of large sizes of 3D data collections becomes an important field. The indexation of such data allows a designer for instance to easily find similar data- in a visual or semantic

Abstract—We present an image retrieval system based on a combined search of text and content. The idea is to use the text present in title, description, and tags of the images for improving the results obtained with a standard content-based search. The system contains two ...

This paper proposes a method for image retrieval and database indexing based on iconic representation which can work using objects at different scales and orientations. The system uses some wellknown computer vision methods, like: perceptual organization, iconic representation based on gaussian filters and dimensionality reduction. An application of this system on a database of human-made objects is also described. Key Words : Image database, Image retrieval, Iconic representation, Perceptual organization, Shape similarity, Dimensionality reduction, Object recognition, Multi-scale recognition. 1 Introduction Nowadays, the ability of computers to rapidly and successfully retrieve images from one or several databases of different objects has a high interest in many applications [Swets and Weng 1996]: digital libraries, WWW (World Wide Web) searches, classify new objects, etc. We are attempting to build a system which might be able to automatically classify new complex images into seve...