Efficient algorithm for image compression to further orthogonal range searching

Image database visualisations, in particular mapping-based visualisations, provide an interesting approach to accessing image repositories as they are able to overcome some of the drawbacks associated with retrieval based approaches. However, making a mapping-based approach work efficiently on large remote image databases, has yet to be explored. In this paper, we present Web-Based Images Browser (WBIB), a novel system that efficiently employs image pyramids to reduce bandwidth requirements so that users can interactively explore 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.

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/.

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.

Software testing is one of the most important Software Engineering processes, being the primary activity to check the conformance between the software requirements and its actual behavior. The automation of software testing activities is essential to certify productivity and effectiveness in such activities. Test automation leads testing activities to be conducted under systematic and accurate criteria, raising the chance of testers to reveal faults or inconsistencies. Test oracles are elementary members in software testing automation, being the mechanism responsible for indicating the correctness of software outputs. In testing environments, test oracles can be effectively implemented based on several sources of information about the Software Under Testing (SUT): software specifications, assertions, formal methods (Finite State Machines (FSM), formal specifications, etc, machine-learning methods, and metamorphic relations. Regardless of the implementation strategy, test oracles are vulnerable to false positive/negative verdicts, configuring what the literature describes as "the oracle problem". Therefore, test oracles are a non-trivial and challenging object of studies of the software engineering research area. SUT's outputs in unusual formats make it harder the oracle problem. Audio, images, three-dimensional objects, virtual reality environments, complex statistical compositions, etc, are examples of non-trivial output formats. In the software testing context, SUTs with unusual outputs can be called "complex-output systems". In this doctorate dissertation, we propose and evaluate a novel test oracle approach for complex-output systems called feature-based test oracles. The purpose of feature-based test oracles is the appropriation of a processing image technique called Content-Based Image Retrieval (CBIR) to collect information from features extracted from the SUT's outputs to compose test oracles. Given a query image, CBIR combines feature extraction and similarity functions to alleviate the problem of searching for digital images in large databases. In previous research, we have integrated CBIR concepts in a testing framework to support the automation of testing activities in processing image systems and systems with Graphical User Interfaces (GUI). In this doctorate dissertation, we extended that framework and its concepts to general complex-output systems, addressing the feature-based test oracle approach. We use Text-To-Speech (TTS) systems to validate empirically our test oracle technique. Through the results of five empirical analyses, three of them conducted in line with problems of a real-world industry TTS system, show the proposed technique is a valuable instrument to automate testing activities and alleviate practitioners' efforts on testing complex output systems. We conclude the proposed test oracles are effective because they systematically evaluate the SUT's sensorial output rather than produce verdicts based on subjective specifications. As future work, we plan to conduct investigations towards the reduction of false positives/negatives and the association of the test oracles with machine learning techniques and metamorphic relations.

Content-based image retrieval (CBIR) has turned into an important and active potential research field with the advance of multimedia and imaging technology. It makes use of image features, such as color, texture and shape, to index images with minimal human intervention. A CBIR system can be used to locate medical images in large databases. In this paper we propose a CBIR system which describes the methodology for retrieving digital human brain magnetic resonance images (MRI) based on textural features and the Adaptive neuro-fuzzy inference system (ANFIS) learning to retrieve similar images from database in two categories: normal and tumoral. A fuzzy classifier has been used, because of the uncertainty in the results of classifier and capacity of learning. ANFIS is a good candidate for our categorization problem. Our proposed CBIR system can locate a query image in the category of normal or tumoral images in the online retrieval part. Finally, using a relevance feedback, we improve the effectiveness of our retrieval system. This research uses the knowledge of the CBIR approach to the application of medical decision support and discrimination between the normal and abnormal medical images based on features. We present and compare the results of the proposed method with the CBIR systems used in recent works. The experimental results indicate that the proposed method is reliable and has high image retrieval efficiency compared with the previous works.

Co-occurrence of adjacent sparse local ternary patterns: A feature descriptor for texture and face image retrieval

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 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.

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.

Query by image content is a method to retrieve the most important images from the image database. It is an answer for the problem of searching for digital images in large database. A large number of relevance feedback schemes have been developed to improve the performance of content based image retrieval. In this paper we propose biased discriminant Euclidean embedding that form intraclass geometry and interclass discrimination. In this method images can be grouped by their similarity. In order to achieve this, firstly the visual features of the images are found out. In addition to this we use two clustering algorithms to assemble the images into clusters using their low level visual features. Here we have to filter the images in the hierarchical clustering and then apply the clustered images to K-Means, so that we can get the most relevant images. Query image is compared directly with the images in the clusters. Therefore number of comparisons is reduced because query image is compared only with the images in the clusters instead of comparing with all images in the database.

We present a content-based image retrieval system, INDI (techniques for Intelligent Navigation in Digital Image databases), that combines the use of low-level pattern recognition techniques, machine learning and an intuitive human-computer interface in order to support intelligent and user-friendly semantic navigation in large image databases. To keep independence from specific image domains and to encompass different search tasks, the system is highly modular and contains a hierarchical mechanism for the adaptive reweighting of similarity measures implemented by dynamically reloadable modules at different semantic levels.