Perceptually relevant browsing environments for large texture databases

This thesis describes the development of a large database of texture stimuli, the production of a similarity matrix re ecting human judgements of similarity about the database, and the development of three browsing models that exploit structure in the perceptual information for navigation. Rigorous psychophysical comparison experiments are carried out and the SOM (Self Organising Map) found to be the fastest of the three browsing models under examination. We investigate scalable methods of augmenting a similarity matrix using the SOM browsing environment to introduce previously unknown textures. Further psychophysical experiments reveal our method produces a data organisation that is as fast to navigate as that derived from the perceptual grouping experiments.Engineering and Physical Sciences Research Council (EPSRC

A Similarity Measure for Material Appearance

We present a model to measure the similarity in appearance between different materials, which correlates with human similarity judgments. We first create a database of 9,000 rendered images depicting objects with varying materials, shape and illumination. We then gather data on perceived similarity from crowdsourced experiments; our analysis of over 114,840 answers suggests that indeed a shared perception of appearance similarity exists. We feed this data to a deep learning architecture with a novel loss function, which learns a feature space for materials that correlates with such perceived appearance similarity. Our evaluation shows that our model outperforms existing metrics. Last, we demonstrate several applications enabled by our metric, including appearance-based search for material suggestions, database visualization, clustering and summarization, and gamut mapping.Comment: 12 pages, 17 figure

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

Image annotation with Photocopain

Photo annotation is a resource-intensive task, yet is increasingly essential as image archives and personal photo collections grow in size. There is an inherent conflict in the process of describing and archiving personal experiences, because casual users are generally unwilling to expend large amounts of effort on creating the annotations which are required to organise their collections so that they can make best use of them. This paper describes the Photocopain system, a semi-automatic image annotation system which combines information about the context in which a photograph was captured with information from other readily available sources in order to generate outline annotations for that photograph that the user may further extend or amend

Content Based Retrieval Using Colour And Texture Of Wavelet Based Compressed Images [TA1637. I67 2008 f rb].

Permintaan yang tinggi terhadap penggunaan dapatan semula imej telah menggalakkan pembangun aplikasi multimedia untuk mencari cara untuk mengurus dan mencari imej dengan lebih efisien. The growing demands for image retrieval in multimedia field such as crime prevention, health informatics and biometrics has pushed application developers to search ways to manage and retrieve images more efficiently

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