Statistical Feature Selection and Extraction for Video and Image Segmentation

The purpose of this study was to develop statistical feature selection and extraction methods for video and image segmentation, which partition a video or image into non-overlap and meaningful objects or regions. It is a fundamental step towards content-based visual information analysis. Visual data segmentation is a difficult task due to the various definitions of meaningful entities, as well as their complex properties and behaviors. Generally, visual data segmentation is a pattern recognition problem, where feature selection/extraction and data classifier design are two key components. Pixel intensity, color, time, texture, spatial location, shape, motion information, etc., are most frequently used features for visual data representation. Since not all of features are representative regarding visual data, and have significant contribution to the data classification, feature selection and/or extraction are necessary to select or generate salient features for data classifier. Statistical machine learning methods play important roles in developing data classifiers. In this report, both parametric and nonparametric machine learning methods are studied under three specific applications: video and image segmentation, as well as remote sensing data analysis. For various visual data segmentation tasks, key-frame extraction in video segmentation, WDHMM likelihood computation, decision tree training, and support vector learning are studied for feature selection and/or extraction and segmentation. Simulations on both synthetic and real data show that the proposed methods can provide accurate and robust segmentation results, as well as representative and discriminative features sets. This work can further inspire our studies towards the real applications. In these applications, we are able to obtain state-of-the-art or promising results as well as efficient algorithmsElectrical Engineering Technolog

Human-Centered Content-Based Image Retrieval

Retrieval of images that lack a (suitable) annotations cannot be achieved through (traditional) Information Retrieval (IR) techniques. Access through such collections can be achieved through the application of computer vision techniques on the IR problem, which is baptized Content-Based Image Retrieval (CBIR). In contrast with most purely technological approaches, the thesis Human-Centered Content-Based Image Retrieval approaches the problem from a human/user centered perspective. Psychophysical experiments were conducted in which people were asked to categorize colors. The data gathered from these experiments was fed to a Fast Exact Euclidean Distance (FEED) transform (Schouten & Van den Broek, 2004), which enabled the segmentation of color space based on human perception (Van den Broek et al., 2008). This unique color space segementation was exploited for texture analysis and image segmentation, and subsequently for full-featured CBIR. In addition, a unique CBIR-benchmark was developed (Van den Broek et al., 2004, 2005). This benchmark was used to explore what and how several parameters (e.g., color and distance measures) of the CBIR process influence retrieval results. In contrast with other research, users judgements were assigned as metric. The online IR and CBIR system Multimedia for Art Retrieval (M4ART) (URL: http://www.m4art.org) has been (partly) founded on the techniques discussed in this thesis. References: - Broek, E.L. van den, Kisters, P.M.F., and Vuurpijl, L.G. (2004). The utilization of human color categorization for content-based image retrieval. Proceedings of SPIE (Human Vision and Electronic Imaging), 5292, 351-362. [see also Chapter 7] - Broek, E.L. van den, Kisters, P.M.F., and Vuurpijl, L.G. (2005). Content-Based Image Retrieval Benchmarking: Utilizing Color Categories and Color Distributions. Journal of Imaging Science and Technology, 49(3), 293-301. [see also Chapter 8] - Broek, E.L. van den, Schouten, Th.E., and Kisters, P.M.F. (2008). Modeling Human Color Categorization. Pattern Recognition Letters, 29(8), 1136-1144. [see also Chapter 5] - Schouten, Th.E. and Broek, E.L. van den (2004). Fast Exact Euclidean Distance (FEED) transformation. In J. Kittler, M. Petrou, and M. Nixon (Eds.), Proceedings of the 17th IEEE International Conference on Pattern Recognition (ICPR 2004), Vol 3, p. 594-597. August 23-26, Cambridge - United Kingdom. [see also Appendix C

Indexing Techniques for Image and Video Databases: an approach based on Animate Vision Paradigm

[ITALIANO]In questo lavoro di tesi vengono presentate e discusse delle innovative tecniche di indicizzazione per database video e di immagini basate sul paradigma della “Animate Vision” (Visione Animata). Da un lato, sarà mostrato come utilizzando, quali algoritmi di analisi di una data immagine, alcuni meccanismi di visione biologica, come i movimenti saccadici e le fissazioni dell'occhio umano, sia possibile ottenere un query processing in database di immagini più efficace ed efficiente. In particolare, verranno discussi, la metodologia grazie alla quale risulta possibile generare due sequenze di fissazioni, a partire rispettivamente, da un'immagine di query I_q ed una di test I_t del data set, e, come confrontare tali sequenze al fine di determinare una possibile misura della similarità (consistenza) tra le due immagini. Contemporaneamente, verrà discusso come tale approccio unito a tecniche classiche di clustering possa essere usato per scoprire le associazioni semantiche nascoste tra immagini, in termini di categorie, che, di contro, permettono un'automatica pre-classificazione (indicizzazione) delle immagini e possono essere usate per guidare e migliorare il processo di query. Saranno presentati, infine, dei risultati preliminari e l'approccio proposto sarà confrontato con le più recenti tecniche per il recupero di immagini descritte in letteratura. Dall'altro lato, sarà mostrato come utilizzando la precedente rappresentazione “foveata” di un'immagine, risulti possibile partizionare un video in shot. Più precisamente, il metodo per il rilevamento dei cambiamenti di shot si baserà sulla computazione, in ogni istante di tempo, della misura di consistenza tra le sequenze di fissazioni generate da un osservatore ideale che guarda il video. Lo schema proposto permette l'individuazione, attraverso l'utilizzo di un'unica tecnica anziché di più metodi dedicati, sia delle transizioni brusche sia di quelle graduali. Vengono infine mostrati i risultati ottenuti su varie tipologie di video e, come questi, validano l'approccio proposto. / [INGLESE]In this dissertation some novel indexing techniques for video and image database based on “Animate Vision” Paradigm are presented and discussed. From one hand, it will be shown how, by embedding within image inspection algorithms active mechanisms of biological vision such as saccadic eye movements and fixations, a more effective query processing in image database can be achieved. In particular, it will be discussed the way to generate two fixation sequences from a query image I_q and a test image I_t of the data set, respectively, and how to compare the two sequences in order to compute a possible similarity (consistency) measure between the two images. Meanwhile, it will be shown how the approach can be used with classical clustering techniques to discover and represent the hidden semantic associations among images, in terms of categories, which, in turn, allow an automatic pre-classification (indexing), and can be used to drive and improve the query processing. Eventually, preliminary results will be presented and the proposed approach compared with the most recent techniques for image retrieval described in the literature. From the other one, it will be discussed how by taking advantage of such foveated representation of an image, it is possible to partitioning of a video into shots. More precisely, the shot-change detection method will be based on the computation, at each time instant, of the consistency measure of the fixation sequences generated by an ideal observer looking at the video. The proposed scheme aims at detecting both abrupt and gradual transitions between shots using a single technique, rather than a set of dedicated methods. Results on videos of various content types are reported and validate the proposed approach

Machine learning methods for discriminating natural targets in seabed imagery

The research in this thesis concerns feature-based machine learning processes and methods for discriminating qualitative natural targets in seabed imagery. The applications considered, typically involve time-consuming manual processing stages in an industrial setting. An aim of the research is to facilitate a means of assisting human analysts by expediting the tedious interpretative tasks, using machine methods. Some novel approaches are devised and investigated for solving the application problems. These investigations are compartmentalised in four coherent case studies linked by common underlying technical themes and methods. The first study addresses pockmark discrimination in a digital bathymetry model. Manual identification and mapping of even a relatively small number of these landform objects is an expensive process. A novel, supervised machine learning approach to automating the task is presented. The process maps the boundaries of ≈ 2000 pockmarks in seconds - a task that would take days for a human analyst to complete. The second case study investigates different feature creation methods for automatically discriminating sidescan sonar image textures characteristic of Sabellaria spinulosa colonisation. Results from a comparison of several textural feature creation methods on sonar waterfall imagery show that Gabor filter banks yield some of the best results. A further empirical investigation into the filter bank features created on sonar mosaic imagery leads to the identification of a useful configuration and filter parameter ranges for discriminating the target textures in the imagery. Feature saliency estimation is a vital stage in the machine process. Case study three concerns distance measures for the evaluation and ranking of features on sonar imagery. Two novel consensus methods for creating a more robust ranking are proposed. Experimental results show that the consensus methods can improve robustness over a range of feature parameterisations and various seabed texture classification tasks. The final case study is more qualitative in nature and brings together a number of ideas, applied to the classification of target regions in real-world sonar mosaic imagery. A number of technical challenges arose and these were surmounted by devising a novel, hybrid unsupervised method. This fully automated machine approach was compared with a supervised approach in an application to the problem of image-based sediment type discrimination. The hybrid unsupervised method produces a plausible class map in a few minutes of processing time. It is concluded that the versatile, novel process should be generalisable to the discrimination of other subjective natural targets in real-world seabed imagery, such as Sabellaria textures and pockmarks (with appropriate features and feature tuning.) Further, the full automation of pockmark and Sabellaria discrimination is feasible within this framework