Extraction of texture features from arbitrary-shaped regions for image retrieval

Lots of work has been done in texture feature extraction for rectangular images, but not as much attention has been paid to the arbitrary-shaped regions available in region-based image retrieval (RBIR) systems. In This work, we present a texture feature extraction algorithm, based on projection onto convex sets (POCS) theory. POCS iteratively concentrates more and more energy into the selected coefficients from which texture features of an arbitrary-shaped region can be extracted. Experimental results demonstrate the effectiveness of the proposed algorithm for image retrieval purposes.

Application of digital image processing techniques to the photometric testing of vehicle headlamps

The aim of this Interdisciplinary Higher Degrees project was the development of a high-speed method of photometrically testing vehicle headlamps, based on the use of image processing techniques, for Lucas Electrical Limited. Photometric testing involves measuring the illuminance produced by a lamp at certain points in its beam distribution. Headlamp performance is best represented by an iso-lux diagram, showing illuminance contours, produced from a two-dimensional array of data. Conventionally, the tens of thousands of measurements required are made using a single stationary photodetector and a two-dimensional mechanical scanning system which enables a lamp's horizontal and vertical orientation relative to the photodetector to be changed. Even using motorised scanning and computerised data-logging, the data acquisition time for a typical iso-lux test is about twenty minutes. A detailed study was made of the concept of using a video camera and a digital image processing system to scan and measure a lamp's beam without the need for the time-consuming mechanical movement. Although the concept was shown to be theoretically feasible, and a prototype system designed, it could not be implemented because of the technical limitations of commercially-available equipment. An alternative high-speed approach was developed, however, and a second prototype syqtem designed. The proposed arrangement again uses an image processing system, but in conjunction with a one-dimensional array of photodetectors and a one-dimensional mechanical scanning system in place of a video camera. This system can be implemented using commercially-available equipment and, although not entirely eliminating the need for mechanical movement, greatly reduces the amount required, resulting in a predicted data acquisiton time of about twenty seconds for a typical iso-lux test. As a consequence of the work undertaken, the company initiated an 80,000 programme to implement the system proposed by the author.

An information foraging theory based user study of an adaptive user interaction framework for content-based image retrieval

This paper presents the design and results of a task-based user study, based on Information Foraging Theory, on a novel user interaction framework - uInteract - for content-based image retrieval (CBIR). The framework includes a four-factor user interaction model and an interactive interface. The user study involves three focused evaluations, 12 simulated real life search tasks with different complexity levels, 12 comparative systems and 50 subjects. Information Foraging Theory is applied to the user study design and the quantitative data analysis. The systematic findings have not only shown how effective and easy to use the uInteract framework is, but also illustrate the value of Information Foraging Theory for interpreting user interaction with CBIR. © 2011 Springer-Verlag Berlin Heidelberg.

Applying Information Foraging Theory to understand user interaction with content-based image retrieval

The paper proposes an ISE (Information goal, Search strategy, Evaluation threshold) user classification model based on Information Foraging Theory for understanding user interaction with content-based image retrieval (CBIR). The proposed model is verified by a multiple linear regression analysis based on 50 users' interaction features collected from a task-based user study of interactive CBIR systems. To our best knowledge, this is the first principled user classification model in CBIR verified by a formal and systematic qualitative analysis of extensive user interaction data. Copyright 2010 ACM.

A four-factor user interaction model for content-based image retrieval

In order to bridge the “Semantic gap”, a number of relevance feedback (RF) mechanisms have been applied to content-based image retrieval (CBIR). However current RF techniques in most existing CBIR systems still lack satisfactory user interaction although some work has been done to improve the interaction as well as the search accuracy. In this paper, we propose a four-factor user interaction model and investigate its effects on CBIR by an empirical evaluation. Whilst the model was developed for our research purposes, we believe the model could be adapted to any content-based search system.

Enabling effective user interactions in content-based image retrieval

This paper presents an interactive content-based image retrieval framework—uInteract, for delivering a novel four-factor user interaction model visually. The four-factor user interaction model is an interactive relevance feedback mechanism that we proposed, aiming to improve the interaction between users and the CBIR system and in turn users overall search experience. In this paper, we present how the framework is developed to deliver the four-factor user interaction model, and how the visual interface is designed to support user interaction activities. From our preliminary user evaluation result on the ease of use and usefulness of the proposed framework, we have learnt what the users like about the framework and the aspects we could improve in future studies. Whilst the framework is developed for our research purposes, we believe the functionalities could be adapted to any content-based image search framework.

Comparing dissimilarity measures for content-based image retrieval

Dissimilarity measurement plays a crucial role in content-based image retrieval, where data objects and queries are represented as vectors in high-dimensional content feature spaces. Given the large number of dissimilarity measures that exist in many fields, a crucial research question arises: Is there a dependency, if yes, what is the dependency, of a dissimilarity measure’s retrieval performance, on different feature spaces? In this paper, we summarize fourteen core dissimilarity measures and classify them into three categories. A systematic performance comparison is carried out to test the effectiveness of these dissimilarity measures with six different feature spaces and some of their combinations on the Corel image collection. From our experimental results, we have drawn a number of observations and insights on dissimilarity measurement in content-based image retrieval, which will lay a foundation for developing more effective image search technologies.

MPEG-7 Based Image Retrieval on the World Wide Web

Due to the rapid growth of the number of digital media elements like image, video, audio, graphics on Internet, there is an increasing demand for effective search and retrieval techniques. Recently, many search engines have made image search as an option like Google, AlltheWeb, AltaVista, Freenet. In addition to this, Ditto, Picsearch, can search only the images on Internet. There are also other domain specific search engines available for graphics and clip art, audio, video, educational images, artwork, stock photos, science and nature [www.faganfinder.com/img]. These entire search engines are directory based. They crawls the entire Internet and index all the images in certain categories. They do not display the images in any particular order with respect to the time and context. With the availability of MPEG-7, a standard for describing multimedia content, it is now possible to store the images with its metadata in a structured format. This helps in searching and retrieving the images. The MPEG-7 standard uses XML to describe the content of multimedia information objects. These objects will have metadata information in the form of MPEG-7 or any other similar format associated with them. It can be used in different ways to search the objects. In this paper we propose a system, which can do content based image retrieval on the World Wide Web. It displays the result in user-defined order.

Distance Matrix Approach to Content Image Retrieval

As the volume of image data and the need of using it in various applications is growing significantly in the last days it brings a necessity of retrieval efficiency and effectiveness. Unfortunately, existing indexing methods are not applicable to a wide range of problem-oriented fields due to their operating time limitations and strong dependency on the traditional descriptors extracted from the image. To meet higher requirements, a novel distance-based indexing method for region-based image retrieval has been proposed and investigated. The method creates premises for considering embedded partitions of images to carry out the search with different refinement or roughening level and so to seek the image meaningful content.

Learning Binary Code Representations for Effective and Efficient Image Retrieval

The size of online image datasets is constantly increasing. Considering an image dataset with millions of images, image retrieval becomes a seemingly intractable problem for exhaustive similarity search algorithms. Hashing methods, which encodes high-dimensional descriptors into compact binary strings, have become very popular because of their high efficiency in search and storage capacity. In the first part, we propose a multimodal retrieval method based on latent feature models. The procedure consists of a nonparametric Bayesian framework for learning underlying semantically meaningful abstract features in a multimodal dataset, a probabilistic retrieval model that allows cross-modal queries and an extension model for relevance feedback. In the second part, we focus on supervised hashing with kernels. We describe a flexible hashing procedure that treats binary codes and pairwise semantic similarity as latent and observed variables, respectively, in a probabilistic model based on Gaussian processes for binary classification. We present a scalable inference algorithm with the sparse pseudo-input Gaussian process (SPGP) model and distributed computing. In the last part, we define an incremental hashing strategy for dynamic databases where new images are added to the databases frequently. The method is based on a two-stage classification framework using binary and multi-class SVMs. The proposed method also enforces balance in binary codes by an imbalance penalty to obtain higher quality binary codes. We learn hash functions by an efficient algorithm where the NP-hard problem of finding optimal binary codes is solved via cyclic coordinate descent and SVMs are trained in a parallelized incremental manner. For modifications like adding images from an unseen class, we propose an incremental procedure for effective and efficient updates to the previous hash functions. Experiments on three large-scale image datasets demonstrate that the incremental strategy is capable of efficiently updating hash functions to the same retrieval performance as hashing from scratch.

The life sciences Global Image Database (GID)

Although a vast amount of life sciences data is generated in the form of images, most scientists still store images on extremely diverse and often incompatible storage media, without any type of metadata structure, and thus with no standard facility with which to conduct searches or analyses. Here we present a solution to unlock the value of scientific images. The Global Image Database (GID) is a web-based (http://www.g wer.ch/qv/gid/gid.htm) structured central repository for scientific annotated images. The GID was designed to manage images from a wide spectrum of imaging domains ranging from microscopy to automated screening. The annotations in the GID define the source experiment of the images by describing who the authors of the experiment are, when the images were created, the biological origin of the experimental sample and how the sample was processed for visualization. A collection of experimental imaging protocols provides details of the sample preparation, and labeling, or visualization procedures. In addition, the entries in the GID reference these imaging protocols with the probe sequences or antibody names used in labeling experiments. The GID annotations are searchable by field or globally. The query results are first shown as image thumbnail previews, enabling quick browsing prior to original-sized annotated image retrieval. The development of the GID continues, aiming at facilitating the management and exchange of image data in the scientific community, and at creating new query tools for mining image data.

A multimedia indexing and retrieval framework for multimedia database systems

The main challenges of multimedia data retrieval lie in the effective mapping between low-level features and high-level concepts, and in the individual users' subjective perceptions of multimedia content. ^ The objectives of this dissertation are to develop an integrated multimedia indexing and retrieval framework with the aim to bridge the gap between semantic concepts and low-level features. To achieve this goal, a set of core techniques have been developed, including image segmentation, content-based image retrieval, object tracking, video indexing, and video event detection. These core techniques are integrated in a systematic way to enable the semantic search for images/videos, and can be tailored to solve the problems in other multimedia related domains. In image retrieval, two new methods of bridging the semantic gap are proposed: (1) for general content-based image retrieval, a stochastic mechanism is utilized to enable the long-term learning of high-level concepts from a set of training data, such as user access frequencies and access patterns of images. (2) In addition to whole-image retrieval, a novel multiple instance learning framework is proposed for object-based image retrieval, by which a user is allowed to more effectively search for images that contain multiple objects of interest. An enhanced image segmentation algorithm is developed to extract the object information from images. This segmentation algorithm is further used in video indexing and retrieval, by which a robust video shot/scene segmentation method is developed based on low-level visual feature comparison, object tracking, and audio analysis. Based on shot boundaries, a novel data mining framework is further proposed to detect events in soccer videos, while fully utilizing the multi-modality features and object information obtained through video shot/scene detection. ^ Another contribution of this dissertation is the potential of the above techniques to be tailored and applied to other multimedia applications. This is demonstrated by their utilization in traffic video surveillance applications. The enhanced image segmentation algorithm, coupled with an adaptive background learning algorithm, improves the performance of vehicle identification. A sophisticated object tracking algorithm is proposed to track individual vehicles, while the spatial and temporal relationships of vehicle objects are modeled by an abstract semantic model. ^

Special issue: Selected papers from the sixth IFIP 2.6 working conference on visual database systems: Guest editors' introduction

While multimedia data, image data in particular, is an integral part of most websites and web documents, our quest for information so far is still restricted to text based search. To explore the World Wide Web more effectively, especially its rich repository of truly multimedia information, we are facing a number of challenging problems. Firstly, we face the ambiguous and highly subjective nature of defining image semantics and similarity. Secondly, multimedia data could come from highly diversified sources, as a result of automatic image capturing and generation processes. Finally, multimedia information exists in decentralised sources over the Web, making it difficult to use conventional content-based image retrieval (CBIR) techniques for effective and efficient search. In this special issue, we present a collection of five papers on visual and multimedia information management and retrieval topics, addressing some aspects of these challenges. These papers have been selected from the conference proceedings (Kluwer Academic Publishers, ISBN: 1-4020- 7060-8) of the Sixth IFIP 2.6 Working Conference on Visual Database Systems (VDB6), held in Brisbane, Australia, on 29–31 May 2002.