Retrieval of Spatially Similar Images using Quadtree-based Indexing

Multimedia applications involving image retrieval demand fast response, which requires efficient database indexing. Generally, a two-level indexing scheme in an image database can help to reduce the search space against a given query image. The first level is required to significantly reduce the search space for the second-stage of comparisons and must be computationally efficient. It is also required to guarantee that no new false negatives may result. In this thesis, we propose a new image signature representation for the first level of a two-level image indexing scheme that is based on hierarchical decomposition of image space into spatial arrangement of image features (quadtrees). We also formally prove that the proposed signature representation scheme not only results in fewer number of matching signatures but also does not result in any new false negative. Further, the performance of the retrieval scheme with proposed ignature representation is evaluated for various feature point detection algorithms

Digital Image Access & Retrieval

The 33th Annual Clinic on Library Applications of Data Processing, held at the University of Illinois at Urbana-Champaign in March of 1996, addressed the theme of "Digital Image Access & Retrieval." The papers from this conference cover a wide range of topics concerning digital imaging technology for visual resource collections. Papers covered three general areas: (1) systems, planning, and implementation; (2) automatic and semi-automatic indexing; and (3) preservation with the bulk of the conference focusing on indexing and retrieval.published or submitted for publicatio

A Location-Aware Middleware Framework for Collaborative Visual Information Discovery and Retrieval

This work addresses the problem of scalable location-aware distributed indexing to enable the leveraging of collaborative effort for the construction and maintenance of world-scale visual maps and models which could support numerous activities including navigation, visual localization, persistent surveillance, structure from motion, and hazard or disaster detection. Current distributed approaches to mapping and modeling fail to incorporate global geospatial addressing and are limited in their functionality to customize search. Our solution is a peer-to-peer middleware framework based on XOR distance routing which employs a Hilbert Space curve addressing scheme in a novel distributed geographic index. This allows for a universal addressing scheme supporting publish and search in dynamic environments while ensuring global availability of the model and scalability with respect to geographic size and number of users. The framework is evaluated using large-scale network simulations and a search application that supports visual navigation in real-world experiments

Indexing, browsing and searching of digital video

Video is a communications medium that normally brings together moving pictures with a synchronised audio track into a discrete piece or pieces of information. The size of a “piece ” of video can variously be referred to as a frame, a shot, a scene, a clip, a programme or an episode, and these are distinguished by their lengths and by their composition. We shall return to the definition of each of these in section 4 this chapter. In modern society, video is ver

Video Indexing and Retrieval Techniques Using Novel Approaches to Video Segmentation, Characterization, and Similarity Matching

Multimedia applications are rapidly spread at an ever-increasing rate introducing a number of challenging problems at the hands of the research community, The most significant and influential problem, among them, is the effective access to stored data. In spite of the popularity of keyword-based search technique in alphanumeric databases, it is inadequate for use with multimedia data due to their unstructured nature. On the other hand, a number of content-based access techniques have been developed in the context of image indexing and retrieval; meanwhile video retrieval systems start to gain wide attention, This work proposes a number of techniques constituting a fully content-based system for retrieving video data. These techniques are primarily targeting the efficiency, reliability, scalability, extensibility, and effectiveness requirements of such applications. First, an abstract representation of the video stream, known as the DC sequence, is extracted. Second, to deal with the problem of video segmentation, an efficient neural network model is introduced. The novel use of the neural network improves the reliability while the efficiency is achieved through the instantaneous use of the recall phase to identify shot boundaries. Third, the problem of key frames extraction is addressed using two efficient algorithms that adapt their selection decisions based on the amount of activity found in each video shot enabling the selection of a near optimal expressive set of key frames. Fourth, the developed system employs an indexing scheme that supports two low-level features, color and texture, to represent video data, Finally, we propose, in the retrieval stage, a novel model for performing video data matching task that integrates a number of human-based similarity factors. All our software implementations are in Java, which enables it to be used across heterogeneous platforms. The retrieval system performance has been evaluated yielding a very good retrieval rate and accuracy, which demonstrate the effectiveness of the developed system

Optimization of signature file parameters for databases with varying record lengths

For signature files we propose a new false drop estimation method for databases with varying record lengths. Our approach provides more accurate estimation of the number of false drops by considering the lengths of individual records instead of using the average number of terms per record. In signature file processing, accurate estimation of the number of false drops is essential to obtain a more accurate signature file and therefore to obtain a better (query) response time. With a formal proof we show that under certain conditions the number of false drops estimated by considering the average record length is less than or equal to the precise 'expected' estimation which is based on the individual record lengths. The experiments with real data show that the proposed method accurately estimates the number of false drops and the actual response time. Depending on the space overhead, our approach obtains up to 33% and 20% response time improvements for the conventional sequential and new efficient multiframe signature file methods, respectively

Similarity Searching in Large Image Databases

We propose a method to handle approximate searching by image content in large image databases. Image content is represented by attributed relational graphs holding features of objects and relationships between objects. The method relies on the assumption that a fixed number of ``labeled'' or ``expected'' objects (e.g., ``heart'', ``lungs'' etc.) are common in all images of a given application domain in addition to a variable number of ``unexpected'' or ``unlabeled'' objects (e.g., ``tumor'', ``hematoma'' etc.). The method can answer queries by example such as ``{\em find all X-rays that are similar to Smith's X-ray}''. The stored images are mapped to points in a multidimensional space and are indexed using state-of-the-art database methods (R-trees). The proposed method has several desirable properties: (a) Database search is approximate so that all images up to a pre-specified degree of similarity (tolerance) are retrieved, (b) it has no ``false dismissals'' (i.e., all images qualifying query selection criteria are retrieved) and (c) it scales-up well as the database grows. We implemented the method and ran experiments on a database of synthetic (but realistic) medical images. The experiments showed that our method significantly outperforms sequential scanning by up to an order of magnitude. (Also cross-referenced as UMIACS-TR-94-134