Exploring mapping-based visualisations of large remote image databases

Without textual descriptions or label information of images, searching semantic concepts in image databases is still a very challenging task. While automatic annotation techniques are yet along way off, we can seek other alternative techniques to solve this difficult issue. In this paper, we propose to learn Web images for searching the semantic concepts in large image databases. To formulate effective algorithms, we suggest to engage the support vector machines for attacking the problem. We evaluate our algorithm in a large image database and demonstrate the preliminary yet promising results.

The paper addresses image feature characterization and the structuring of large and heterogeneous image databases through the stochasticity or randomness appearance. Measuring stochasticity involves finding suitable representations that can significantly reduce statistical dependencies of any order. Wavelet packet representations provide such a framework for a large class of stochastic processes through an appropriate dictionary of parametric models. From this dictionary and the Kolmogorov stochasticity index, the paper proposes semantic stochasticity templates upon wavelet packet sub-bands in order to provide high level classification and content-based image retrieval. The approach is shown to be relevant for texture images.

BackgroundThere is increasing interest in the development of computational methods to analyze fluorescent microscopy images and enable automated large-scale analysis of the subcellular localization of proteins. Determining the subcellular localization is an integral part of identifying a protein's function, and the application of bioinformatics to this problem provides a valuable tool for the annotation of proteomes. Training and validating algorithms used in image analysis research typically rely on large sets of image data, and would benefit from a large, well-annotated and highly-available database of images and associated metadata.DescriptionThe Yeast Resource Center Public Image Repository (YRC PIR) is a large database of images depicting the subcellular localization and colocalization of proteins. Designed especially for computational biologists who need large numbers of images, the YRC PIR contains 532,182 TIFF images from nearly 85,000 separate experiments and their associated experimental data. All images and associated data are searchable, and the results browsable, through an intuitive web interface. Search results, experiments, individual images or the entire dataset may be downloaded as standards-compliant OME-TIFF data.ConclusionsThe YRC PIR is a powerful resource for researchers to find, view, and download many images and associated metadata depicting the subcellular localization and colocalization of proteins, or classes of proteins, in a standards-compliant format. The YRC PIR is freely available at http://images.yeastrc.org/.

Fast retrieval of images from large databases especially from large medical image databases is of great interest to researchers. We have developed shape-based image retrieval system for spine X-ray images that applies Dynamic Programming (DP) in Partial Shape Matching (PSM) techniques to vertebral boundary data. As we enable Internet access to this system with an aim to enable CBIR access to the entire NHANES II spine X-ray image collection, retrieval efficiency has become critical. In this paper, we present enhancements to our existing sequential retrieval model to provide faster partialshape- based vertebrae retrieval. Based on the characteristics of vertebral osteophyte pathology, anterior superior and anterior inferior parts are the areas with the most interest to the users and are indexed for each shape. Pair-wise distances between indexed parts are pre-calculated and used by agglomerative clustering algorithm to index the whole database. To increase the retrieval speed, PSM with DP is therefore conducted only on a small selected set of shapes by the pre-indexing retrieval.

The main objective of image browsers is to empower users to find a desired image with ease, speed and accuracy from a large database. In this paper we present a novel approach at creating an image browsing environment based on human perception with the aim of providing intuitive image navigation. In our approach, similarity judgments form the basic structural organization for the images in our browser. To enrich this we have developed a scalable crowd sourced method of augmenting a database with a large number of additional samples by capturing human judgments from members of a crowd. Experiments were conducted involving two databases that demonstrate the effectiveness of our method as an intuitive, fast browsing environment for large image databases.

Many of the near-duplicate (ND) image detection methods involve a greater number of interest points (IPs) and large dimensions of the feature descriptors requiring huge computations and are unsuitable for large image databases. They may fail to detect NDs if the query and images in the database contain sparse IPs due to low entropy. Besides, the k-means algorithm used for the quantization of visual words may land at a sub-optimal minimum for descriptors because of their distance distribution in feature space. This article presents a new ND image detection method, which uniformly distributes the IPs over low and high entropy regions, reduces the dimension of feature descriptors using discrete wavelet transform (DWT) and employs Seagull Optimization algorithm (SOA) for optimally forming the visual words. It examines proposed method performs on image databases of various sizes and shows that the developed method is more reliable and computationally efficient than the alternatives.

Performance Comparison in Image Retrieval Using Geometric Histogram and K-Means Algorithm

In this paper we address the problem of retrieving images from large image databases, giving a partial description of the image content. This approach allows a limited automatic analysis for image belonging to a domain described in advance to the system using a formalism based on fuzzy sets. The image query processing is based on special access structures generated from the image analysis process.

With the advancement of multimedia technology and the internet, numerous applications have arisen which require the storage and retrieval of large image and video databases. A novel method (Eigenwavelet) was developed to retrieve images from a large heterogeneous image database upon a user-specified query. The queries are in the form of an image(s) that the user seeks to find similar matches to in the database. Using the queries, an efficient algorithm was developed which decomposed each image in the database using wavelet packet analysis. Along each node of the packet tree, Principal Component Analysis was applied to the database images after wavelet packet decomposition, and a set of eigenvectors were generated for each node of the packet tree. To search the image database, the query images are projected onto these eigenvectors (Eigenwavelet coefficients). A distance metric is computed between the projections of the queries and the projections of the images in the database onto the eigenwavelets. Those images with minimal distance (L1) are retrieved in response to a unique query set. Simulations with a heterogeneous image database suggest the Eigenwavelet method of image retrieval is a robust and computationally tractable method of retrieving images with a probability of detection >.8.

This paper considers the problem of compact storing images in large image databases and the methods for orthogonal range searching queries to analyze them. Usually these items are considered separately in the literature. In the present article we describe the formulation of realistic orthogonal range searching and develop the method for compressing raster images with possibility to perform further orthogonal range searching directly on coded image without decompression that can be efficient for large image databases.

Multimedia information and its databases are becoming an essential element for computer and telecommunication consideration in today’s world. Application areas in which attribute-based storage and retrieval is becoming a principal activity are among others, TV productions, video on demand, home entertainment, art galleries and museum management, architectural and engineering design, geographic information systems, finger print and image matching, find out citizenship by election card detail and so on. Other applications are related to the development of information superhighways. When the bandwidth needed for interactive multimedia will be available at home, each user could access a remote server containing very large image and video databases. In this scenario attribute-based storage and retrieval assumes a fundamental role. Also Multimedia Information Systems [7] can benefit of attribute-based retrieval technology; although they will mainly provide techniques to manage and distribute large amounts of multi-source multimedia information, automatic storage by attributes of images and data.

This paper studies image-based geo-localization (IBL) problem using ground-to-aerial cross-view matching. The goal is to predict the spatial location of a ground-level query image by matching it to a large geotagged aerial image database (e.g., satellite imagery). This is a challenging task due to the drastic differences in their viewpoints and visual appearances. Existing deep learning methods for this problem have been focused on maximizing feature similarity between spatially close-by image pairs, while minimizing other images pairs which are far apart. They do so by deep feature embedding based on visual appearance in those ground-and-aerial images. However, in everyday life, humans commonly use {\em orientation} information as an important cue for the task of spatial localization. Inspired by this insight, this paper proposes a novel method which endows deep neural networks with the `commonsense' of orientation. Given a ground-level spherical panoramic image as query input (and a large georeferenced satellite image database), we design a Siamese network which explicitly encodes the orientation (i.e., spherical directions) of each pixel of the images. Our method significantly boosts the discriminative power of the learned deep features, leading to a much higher recall and precision outperforming all previous methods. Our network is also more compact using only 1/5th number of parameters than a previously best-performing network. To evaluate the generalization of our method, we also created a large-scale cross-view localization benchmark containing 100K geotagged ground-aerial pairs covering a city. Our codes and datasets are available at \url{https://github.com/Liumouliu/OriCNN}.

The proliferation of computer technology and digital image‐acquisition hardware has led to the widespread use of image data across a variety of applications including astronomy, art, natural resources, engineering design, military, business operation, medicine, education, etc. Major research activities in the digital image databases surged after the U.S. government's Digital Library Initiative (DLI) from 1994 to 1998. The DLI research team at the University of California at Berkeley developed a work‐centered digital information system that contains 450 digital text documents, 200 air photos, and over 11,000 ground photographs. The system allowed a user or a working group to access its own collections of varying data types, and to generate new materials to be added to the collection. The DLI project at the University of California (UC) at Santa Barbara focused on the development of digital databases that contain geographically referenced materials such as maps and aerial photos. The research team developed a number of tools for browsing and retrieving map images at multiple resolution. Two other major efforts in developing digital image databases were at The National Library of Medicine (NLM), a component of the National Institutes of Health (NIH), and at Time Warner. More recently, research scientists at the University of California at Berkeley and the Fine Arts Museums of San Francisco successfully launched on the Internet the largest art image database in the world, the Thinker ImageBase.Automatic image indexing and retrieving technology is fundamental in digital image databases. There are two general strategies in image retrieval, browsing and searching. Browsing is an information retrieval strategy in which a user navigates through an ordered arrangement of images by making selections from the progressive levels of a hierarchy into which the available images have been logically grouped. Image browsing relies on the user's cognitive abilities to recognize images of interest without having to formulate a specific query. Searching is an information retrieval strategy in which the user communicates with an information retrieval system by an interface that requires the user to input a search query.Images can be indexed and retrieved by text‐tag information and/or by image content. Conventionally, images are indexed using text‐tag information, such as title, key words, date of the work, artist, author, photographer, legend, captions, etc. Images are often subject to a wide range of interpretations, and textual descriptions can only begin to capture the richness and complexity of the semantic content of a visual image. In many applications, the complexity of the information embedded in image content such as types of objects, object attributes, and spatial relationship of objects, etc, cannot be synthesized in a few key words. It has been reported that users querying an image collection tend to be much more specific in their requests and information needs than when querying a text database. Furthermore, text indexing requires large human effort in creating the meta‐data that enables visual queries and is language dependent. Large databases containing thousands and millions of digital images that can occupy gigabytes of space are almost impossible for manual indexing and searching. The demand for systems using pictorial information combined with textual description in image retrieval is growing and automatic indexing and retrieval based on image content has become the most promising techniques for large image databases and digital image libraries. This article describes the techniques developed for content‐based retrieval.

We describe a hierarchical linear subspace method to query large on-line image databases using image similarity as the basis of the queries. The method is based on the generic multimedia indexing (GEMINI) approach which is used in the IBM query through the image content search system. Our approach is demonstrated on image indexing, in which the subspaces correspond to different resolutions of the images. During content-based image retrieval, the search starts in the subspace with the lowest resolution of the images. In this subspace, the set of all possible similar images is determined. In the next subspace, additional metric information corresponding to a higher resolution is used to reduce this set. This procedure is repeated until the similar images can be determined. For evaluation we used three image databases and two different subspace sequences.

The image data has grown fast in fields like social media, healthcare, and e-commerce and this has enhanced the demand of quick and precise image retrieval systems. Conventional methods of retrieval that have relied on handcrafted methods of feature extraction and pixel-wise comparison are costly in computer time and cannot withstand changes in image quality, lighting, and view point. To resolve these drawbacks, this project adopts an effective content-based image retrieval framework with a Vision Transformer (ViT) to extract features and FAISS (Facebook AI Similarity Search) to conducted fast similarity search. The system gets compact numerical representations of features of images as a result of a deep learning model and constructs a scale index to search similarity. The FAISS IndexIVFFlat architecture allows to search the nearest-neighbor approximationally with poor scale and low query time by grouping image features into clusters and lessening the search space at query time. Metadata of the indexed images are stored in the system and the top-k most similar images are retrieved according to the distance scores. This method is accurate and fast thus appropriate when dealing with large scale image databases. The system relies on the IndexIVFFlat structure of FAISS to cluster image features into regions which is a significant reduction of the search space that can support meaningful approximate nearest-neighbor queries. Results indicate that the system can be effective in separating useful and useless images that have high similarity (up to 0.819) and low distances (0.220) that ensure the accuracy and scalability of the system to image databases of large scales.