Savvas A. Chatzichristofis

Lire: lucene image retrieval: an extensible java CBIR library

LIRe (Lucene Image Retrieval) is a light weight open source Java library for content based image retrieval. It provides common and state of the art global image features and offers means for indexing and retrieval. Due to the fact that it is based on a light weight embedded text search engine, it can be integrated easily in applications without relying on a database server.

FCTH: Fuzzy Color and Texture Histogram - A Low Level Feature for Accurate Image Retrieval

This paper deals with the extraction of a new low level feature that combines, in one histogram, color and texture information. This feature is named FCTH - Fuzzy Color and Texture Histogram - and results from the combination of 3 fuzzy systems. FCTH size is limited to 72 bytes per image, rendering this descriptor suitable for use in large image databases. The proposed feature is appropriate for accurately retrieving images even in distortion cases such as deformations, noise and smoothing. It is tested on a large number of images selected from proprietary image databases or randomly retrieved from popular search engines. To evaluate the performance of the proposed feature, the averaged normalized modified retrieval rank was used. An online demo that implements the proposed feature in an image retrieval system is available at: http://orpheus.ee.duth.gr/image_retrieval.

Vision-Controlled Micro Flying Robots: From System Design to Autonomous Navigation and Mapping in GPS-Denied Environments

Autonomous microhelicopters will soon play a major role in tasks like search and rescue, environment monitoring, security surveillance, and inspection. If they are further realized in small scale, they can also be used in narrow outdoor and indoor environments and represent only a limited risk for people. However, for such operations, navigating based only on global positioning system (GPS) information is not sufficient. Fully autonomous operation in cities or other dense environments requires microhelicopters to fly at low altitudes, where GPS signals are often shadowed, or indoors and to actively explore unknown environments while avoiding collisions and creating maps. This involves a number of challenges on all levels of helicopter design, perception, actuation, control, and navigation, which still have to be solved. The Swarm of Micro Flying Robots (SFLY) project was a European Union-funded project with the goal of creating a swarm of vision-controlled microaerial vehicles (MAVs) capable of autonomous navigation, three-dimensional (3-D) mapping, and optimal surveillance coverage in GPS-denied environments. The SFLY MAVs do not rely on remote control, radio beacons, or motion-capture systems but can fly all by themselves using only a single onboard camera and an inertial measurement unit (IMU). This article describes the technical challenges that have been faced and the results achieved from hardware design and embedded programming to vision-based navigation and mapping, with an overview of how all the modules work and how they have been integrated into the final system. Code, data sets, and videos are publicly available to the robotics community. Experimental results demonstrating three MAVs navigating autonomously in an unknown GPS-denied environment and performing 3-D mapping and optimal surveillance coverage are presented.

Img(Rummager): An Interactive Content Based Image Retrieval System

This paper presents an image retrieval suite called img(Rummager) which brings into effect a number of new as well as state of the art descriptors. The application can execute an image search based on a query image, either from XML-based index ¿les, or directly from a folder containing image ¿les, extracting the comparison features in real time. In addition the img(Rummager) application can execute a hybrid search of images from the application server, combining keyword information and visual similarity. Also img(Rummager) supports easy retrieval evaluation based on the normalized modi¿ed retrieval rank (NMRR) and average precision (AP).

Image moment invariants as local features for content based image retrieval using the Bag-of-Visual-Words model

A new image descriptor specifically designed for image retrieval tasks is introduced.Evaluation of affine moment invariants in the area of image retrieval.The usage of image chromaticities improves the overall retrieval performance. This paper presents an image retrieval framework that uses affine image moment invariants as descriptors of local image areas. Detailed feature vectors are generated by feeding the produced moments into a Bag-of-Visual-Words representation. Image moment invariants have been selected for their compact representation of image areas as well as due to their ability to remain unchanged under affine image transformations. Three different setups were examined in order to evaluate and discuss the overall approach. The retrieval results are promising compared with other widely used local descriptors, allowing the proposed framework to serve as a reference point for future image moment local descriptors applied to the general task of content based image retrieval.

Content based radiology image retrieval using a fuzzy rule based scalable composite descriptor

The rapid advances made in the field of radiology, the increased frequency in which oncological diseases appear, as well as the demand for regular medical checks, led to the creation of a large database of radiology images in every hospital or medical center. There is now an imperative need to create an effective method for the indexing and retrieval of these images. This paper proposes a new method of content based radiology medical image retrieval. The description of images relies on a Fuzzy Rule Based Compact Composite Descriptor (CCD), which includes global image features capturing both brightness and texture characteristics in a 1D Histogram. Furthermore, the proposed descriptor includes the spatial distribution of the information it describes. The most important feature of the proposed descriptor is that its size adapts according to the storage capabilities of the application that uses it. Experiments carried out on a large group of images show that even at 48 bytes per image, the proposed descriptor demonstrates a high level of accuracy in its results. To evaluate the performance of the proposed feature, the mean average precision was used.

Co.Vi.Wo.: Color Visual Words Based on Non-Predefined Size Codebooks

Due to the rapid development of information technology and the continuously increasing number of available multimedia data, the task of retrieving information based on visual content has become a popular subject of scientific interest. Recent approaches adopt the bag-of-visual-words (BOVW) model to retrieve images in a semantic way. BOVW has shown remarkable performance in content-based image retrieval tasks, exhibiting better retrieval effectiveness over global and local feature (LF) representations. The performance of the BOVW approach depends strongly, however, on predicting the ideal codebook size, a difficult and database-dependent task. The contribution of this paper is threefold. First, it presents a new technique that uses a self-growing and self-organized neural gas network to calculate the most appropriate size of a codebook for a given database. Second, it proposes a new soft-weighting technique, whereby each LF is classified into only one visual word (VW) with a degree of participation. Third, by combining the information derived from the method that automatically detects the number of VWs, the soft-weighting method, and a color information extraction method from the literature, it shapes a new descriptor, called color VWs. Experimental results on two well-known benchmarking databases demonstrate that the proposed descriptor outperforms 15 contemporary descriptors and methods from the literature, in terms of both precision at K and its ability to retrieve the entire ground truth.

img(Anaktisi): A Web Content Based Image Retrieval System

img(Anaktisi) is a C#/.NET content base image retrieval application suitable for the web. It provides ef¿cient retrieval services for various image databases using as a query a sample image, an image sketched by the user and keywords. The image retrieval engine is powered by innovative compact and effective descriptors. Also, an Auto Relevance Feedback (ARF) technique is provided to the user. This technique readjusts the initial retrieval results based on user preferences improving the retrieval score signi¿cantly. img(Anaktisi) can be found at http://www.anaktisi.net

Automatic Image Annotation and Retrieval Using the Joint Composite Descriptor

Capable tools are needed in order to successfully search and retrieve a suitable image from large image collections. Many content-based image retrieval systems employ low-level image features such as color, texture and shape in order to locate the image. Although the above approaches are successful, they lack the ability to include human perception in the query for retrieval because the query must be an image. In this paper a new image annotation technique and a keyword-based image retrieval system are presented, which map the low-level features of the Joint Composite Descriptor to the high-level features constituted by a set of keywords. One set consists of colors-keywords and the other set consists of words. Experiments were performed to demonstrate the effectiveness of the proposed technique.

SFly: Swarm of micro flying robots

The SFly project is an EU-funded project, with the goal to create a swarm of autonomous vision controlled micro aerial vehicles. The mission in mind is that a swarm of MAVs autonomously maps out an unknown environment, computes optimal surveillance positions and places the MAVs there and then locates radio beacons in this environment. The scope of the work includes contributions on multiple different levels ranging from theoretical foundations to hardware design and embedded programming. One of the contributions is the development of a new MAV, a hexacopter, equipped with enough processing power for onboard computer vision. A major contribution is the development of monocular visual SLAM that runs in real-time onboard of the MAV. The visual SLAM results are fused with IMU measurements and are used to stabilize and control the MAV. This enables autonomous flight of the MAV, without the need of a data link to a ground station. Within this scope novel analytical solutions for fusing IMU and vision measurements have been derived. In addition to the realtime local SLAM, an offline dense mapping process has been developed. For this the MAVs are equipped with a payload of a stereo camera system. The dense environment map is used to compute optimal surveillance positions for a swarm of MAVs. For this an optimiziation technique based on cognitive adaptive optimization has been developed. Finally, the MAVs have been equipped with radio transceivers and a method has been developed to locate radio beacons in the observed environment.