MUCKE aims to mine a large volume of images, to structure them conceptually and to use this conceptual structuring in order to improve large-scale image retrieval. The last decade witnessed important progress concerning low-level image representations. However, there are a number problems which need to be solved in order to unleash the full potential of image mining in applications. The central problem with low-level representations is the mismatch between them and the human interpretation of image content. This problem can be instantiated, for instance, by the incapability of existing descriptors to capture spatial relationships between the concepts represented or by their incapability to convey an explanation of why two images are similar in a content-based image retrieval framework. We start by assessing existing local descriptors for image classification and by proposing to use co-occurrence matrices to better capture spatial relationships in images. The main focus in MUCKE is on cleaning large scale Web image corpora and on proposing image representations which are closer to the human interpretation of images. Consequently, we introduce methods which tackle these two problems and compare results to state of the art methods. Note: some aspects of this deliverable are withheld at this time as they are pending review. Please contact the authors for a preview.

http://ieeexplore.ieee.org/iel2/4524/12820/00592460.pdf

Image Processing

A survey of content-based image retrieval with high-level semantics

This chapter introduces the subject of statistical pattern recognition (SPR). It starts by considering how features are defined and emphasizes that the nearest neighbor algorithm achieves error rates comparable with those of an ideal Bayes’ classifier. The concepts of an optimal number of features, representativeness of the training data, and the need to avoid overfitting to the training data are stressed. The chapter shows that methods such as the support vector machine and artificial neural networks are subject to these same training limitations, although each has its advantages. For neural networks, the multilayer perceptron architecture and back-propagation algorithm are described. The chapter distinguishes between supervised and unsupervised learning, demonstrating the advantages of the latter and showing how methods such as clustering and principal components analysis fit into the SPR framework. The chapter also defines the receiver operating characteristic, which allows an optimum balance between false positives and false negatives to be achieved.

Statistical Pattern Recognition

The appetite for up-to-date information about earth’s surface is ever increasing, as such information provides a base for a large number of applications, including local, regional and global resources monitoring, land-cover and land-use change monitoring, and environmental studies. The data from remote sensing satellites provide opportunities to acquire information about land at varying resolutions and has been widely used for change detection studies. A large number of change detection methodologies and techniques, utilizing remotely sensed data, have been developed, and newer techniques are still emerging. This paper begins with a discussion of the traditionally pixel-based and (mostly) statistics-oriented change detection techniques which focus mainly on the spectral values and mostly ignore the spatial context. This is succeeded by a review of object-based change detection techniques. Finally there is a brief discussion of spatial data mining techniques in image processing and change detection from remote sensing data. The merits and issues of different techniques are compared. The importance of the exponential increase in the image data volume and multiple sensors and associated challenges on the development of change detection techniques are highlighted. With the wide use of very-high-resolution (VHR) remotely sensed images, object-based methods and data mining techniques may have more potential in change detection.

Change detection from remotely sensed images: From pixel-based to object-based approaches

Video indexing and retrieval have a wide spectrum of promising applications, motivating the interest of researchers worldwide. This paper offers a tutorial and an overview of the landscape of general strategies in visual content-based video indexing and retrieval, focusing on methods for video structure analysis, including shot boundary detection, key frame extraction and scene segmentation, extraction of features including static key frame features, object features and motion features, video data mining, video annotation, video retrieval including query interfaces, similarity measure and relevance feedback, and video browsing. Finally, we analyze future research directions.

A Survey on Visual Content-Based Video Indexing and Retrieval

Explosives detection systems (EDS) for aviation security

This paper proposes a rail track inspection technique using automated video analysis. This system is aimed to replace manual visual checks performed by the railway engineers for track inspection. We suggest a combination of image processing and analysis methods to achieve high performance automated rail track inspection. This paper addresses the problem of finding missing clips and finding blue clips which have been recently replaced in place of damaged clips. The experimental results show high performance in machine vision based inspection on a large sample of real train video.

Autonomous Rail Track Inspection using Vision Based System

Recognition of naturally occurring objects is a challenging task In particular, the recognition of clouds is particularly challenging as the texture of such objects is extremely variable under different atmospheric conditions There are several benefits of a practical system that can detect and recognise clouds in natural images especially for applications such as air traffic control In this paper, we test well-known texture feature extraction approaches for automatically training a classifier system to recognise cumulus, towering cumulus, cumulo-nimbus clouds, sky and other clouds For cloud recognition, we use a total of five different feature extraction methods, namely autocorrelation, co-occurrence matrices, edge frequency, Law’s features and primitive length We use the k-nearest neighbour and neural network classifiers for identifying cloud types in test images This exhaustive testing gives us a better understanding of the strengths and limitations of different feature extraction methods and classification techniques on the given problem In particular, we find that no single feature extraction method is best suited for recognising all classes Each method has its own merits We discuss these merits individually and suggest further improvements in this difficult area

Automated ground-based cloud recognition

State-of-the-art on spatio-temporal information-based video retrieval

In this paper we compare some of the traditional, and some fairly new, techniques of texture analysis on the MeasTex and VisTex benchmarks to illustrate their relative abilities. The methods considered include autocorrelation (ACF), co-occurrence matrices (CM), edge frequency (EF), Law's masks (LM), run length (RL), binary stack method (BSM), texture operators (TO), and texture spectrum (TS). In addition, we illustrate the advantage of using feature selection on a combined set that improves the overall recognition performance.

Maneesha Singh

Papers

Explosives detection systems (EDS) for aviation security

Autonomous Rail Track Inspection using Vision Based System

Automated ground-based cloud recognition

State-of-the-art on spatio-temporal information-based video retrieval

Spatial texture analysis: a comparative study