Analyzing scenery images by monotonic tree

Content-based image retrieval (CBIR) has been an active research area in the last ten years, and a variety of techniques have been developed. However, retrieving images on the basis of low-level features has proven unsatisfactory, and new techniques are needed to support high-level queries. Research efforts are needed to bridge the gap between high-level semantics and low-level features. In this paper, we present a novel approach to support semantics-based image retrieval. Our approach is based on the monotonic tree, a derivation of the contour tree for use with discrete data. The structural elements of an image are modeled as branches (or subtrees) of the monotonic tree. These structural elements are classified and clustered on the basis of such properties as color, spatial location, harshness and shape. Each cluster corresponds to some semantic feature. This scheme is applied to the analysis and retrieval of scenery images. Comparisons of experimental results of this approach with conventional techniques using low-level features demonstrate the effectiveness of our approach.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/42315/1/30080495.pd

Automatic Annotation and Retrieval of Images

Although a variety of techniques have been developed for content-based image retrieval (CBIR), automatic image retrieval by semantics still remains a challenging problem. We propose a novel approach for semantics-based image annotation and retrieval. Our approach is based on the monotonic tree model. The branches of the monotonic tree of an image, termed as structural elements , are classified and clustered based on their low level features such as color, spatial location, coarseness, and shape. Each cluster corresponds to some semantic feature. The category keywords indicating the semantic features are automatically annotated to the images. Based on the semantic features extracted from images, high-level (semantics-based) querying and browsing of images can be achieved. We apply our scheme to analyze scenery features. Experiments show that semantic features, such as sky, building, trees, water wave, placid water, and ground, can be effectively retrieved and located in images.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/41397/1/11280_2004_Article_5122908.pd

Hierarchical Image Representation Simplification Driven by Region Complexity

International audienceThis article presents a technique that arranges the elements of hierarchical representations of images according to a coarseness attribute. The choice of the attribute can be made according to prior knowledge about the content of the images and the intended application. The transformation is similar to filtering a hierarchy with a non-increasing attribute, and comprises the results of multiple simple filterings with an increasing attribute. The transformed hierarchy can be used for search space reduction prior to the image analysis process because it allows for direct access to the hierarchy elements at the same scale or a narrow range of scales

Window Room

I am always fascinated by the space created by an architectural opening. In the past, people simply adored window spaces. They created various attractive scenes to surround the window, such as sitting in a bay window, getting sunlight in a glazed alcove and looking at a garden through widely opened sliding windows. A hole in the wall is not just for letting air and lights in or to keep out rain and dust out, but an essential element that has numerous spatial qualities. In this book, I will focus on embodying a potential of a window into an interior component that broadens possibilities of space. What is the role of window space and how can this particular depth in a room evoke activity? Window Room creates an experience, which stimulates your senses. We have lost its depth and the unobtrusive framed view; windows have become huge glass walls and the walls tend to be thinner. After the loss of all atmospheric window spaces, how to bring this quality back, becomes my main challenge. My intention also lies on articulating the identity of a window as a room. During the process, I found out that there are many examples focusing on the existence of windows as a comfortable seating place, but not many of them dealing with it as another room that inhabit a room. The book consists of three parts. The first part explores various window features and scenes from paintings to architecture. The second part illustrates the concept of Window Room as a detached room in a room. The last part is about the execution of Window Room project

Big data and the SP theory of intelligence

This article is about how the "SP theory of intelligence" and its realisation in the "SP machine" may, with advantage, be applied to the management and analysis of big data. The SP system -- introduced in the article and fully described elsewhere -- may help to overcome the problem of variety in big data: it has potential as "a universal framework for the representation and processing of diverse kinds of knowledge" (UFK), helping to reduce the diversity of formalisms and formats for knowledge and the different ways in which they are processed. It has strengths in the unsupervised learning or discovery of structure in data, in pattern recognition, in the parsing and production of natural language, in several kinds of reasoning, and more. It lends itself to the analysis of streaming data, helping to overcome the problem of velocity in big data. Central in the workings of the system is lossless compression of information: making big data smaller and reducing problems of storage and management. There is potential for substantial economies in the transmission of data, for big cuts in the use of energy in computing, for faster processing, and for smaller and lighter computers. The system provides a handle on the problem of veracity in big data, with potential to assist in the management of errors and uncertainties in data. It lends itself to the visualisation of knowledge structures and inferential processes. A high-parallel, open-source version of the SP machine would provide a means for researchers everywhere to explore what can be done with the system and to create new versions of it.Comment: Accepted for publication in IEEE Acces

Modeling Efficient Classification as a Process of Confidence Assessment and Delegation

In visual object detection and recognition, classifiers have two interesting characteristics: accuracy and speed. Accuracy depends on the complexity of the image features and classifier decision surfaces. Speed depends on the hardware and the computational effort required to use the features and decision surfaces. When attempts to increase accuracy lead to increases in complexity and effort, it is necessary to ask how much are we willing to pay for increased accuracy. For example, if increased computational effort implies quickly diminishing returns in accuracy, then those designing inexpensive surveillance applications cannot aim for maximum accuracy at any cost. It becomes necessary to find trade-offs between accuracy and effort. We study efficient classification of images depicting real-world objects and scenes. Classification is efficient when a classifier can be controlled so that the desired trade-off between accuracy and effort (speed) is achieved and unnecessary computations are avoided on a per input basis. A framework is proposed for understanding and modeling efficient classification of images. Classification is modeled as a tree-like process. In designing the framework, it is important to recognize what is essential and to avoid structures that are narrow in applicability. Earlier frameworks are lacking in this regard. The overall contribution is two-fold. First, the framework is presented, subjected to experiments, and shown to be satisfactory. Second, certain unconventional approaches are experimented with. This allows the separation of the essential from the conventional. To determine if the framework is satisfactory, three categories of questions are identified: trade-off optimization, classifier tree organization, and rules for delegation and confidence modeling. Questions and problems related to each category are addressed and empirical results are presented. For example, related to trade-off optimization, we address the problem of computational bottlenecks that limit the range of trade-offs. We also ask if accuracy versus effort trade-offs can be controlled after training. For another example, regarding classifier tree organization, we first consider the task of organizing a tree in a problem-specific manner. We then ask if problem-specific organization is necessary.Tässä työssä käsitellään kuvien automaattista luokittelua tehokkuuden näkökulmasta. Luokittelulla tarkoitetaan sitä, että kuville annetaan otsikoita ennalta sovitusta otsikoiden joukosta. Esimerkiksi kasvojen etsinnässä kuvia voidaan luokitella kasvokuviksi tai taustakuviksi. Luokittelussa käytettävillä ohjelmilla, eli luokittelijoilla, on kaksi mielenkiintoista ominaisuutta: tarkkuus ja nopeus. Tarkkuudella tarkoitetaan todennäköisyyttä ennustaa kuvan luokka oikein. Tarkkuus riippuu kuvista etsittävien piirteiden ja luokittelijan käyttämien päätössääntöjen monimutkaisuudesta. Nopeus puolestaan riippuu käytettävästä laitteistosta ja luokittelijan laskennallisesta vaativuudesta. Kun tarkkuuden kasvattaminen johtaa monimutkaisuuden ja laskennallisen vaativuuden kasvuun, on tarpeen harkita johtaako muutos haluttuun lopputulokseen. Jos esimerkiksi vaativuuden annetaan kasvaa merkittävästi, mutta tarkkuus paranee vain vähän, niin ei ole mahdollista tavoitella mahdollisimman tarkkoja ja samalla halpoja sovelluksia. Tällöin tarvitaan hallittuja vaihtokauppoja tarkkuuden ja nopeuden välillä. Tässä työssä luokittelua sanotaan tehokkaaksi silloin, kun luokittelija voidaan säätää saavuttamaan haluttu vaihtokauppa tarkkuuden ja nopeuden välillä. Työssä ehdotetaan tiettyä mallinnuskehystä tehokkaan luokittelun mallintamiseksi ja ymmärtämiseksi. Luokittelua mallinnetaan puun kaltaisena prosessina, jossa kuvat kulkeutuvat juurisolmusta lehtisolmuihin päin. Puun säädettävistä parametreista riippuu kuinka syvälle puuhun kuvat kulkeutuvat. Syvyys on eräs nopeuteen ja tarkkuuteen vaikuttavista tekijöistä. Puun rakenne voi mukailla esimerkiksi luokkien hierarkiaa. Työn kokonaiskontribuutio on kaksitahoinen. Ensiksi mallinnuskehys esitetään ja osoitetaan kokeellisesti tyydyttäväksi. Toiseksi työssä kokeillaan tiettyjä epätavallisia lähestymistapoja osaongelmiin. Jälkimmäisen takia saadaan selville mitä mallinnuskehyksessä tarvitaan ja mitä ei. Työssä tarkastellaan muun muossa laskennallisten pullonkaulojen muodostumista puihin, vaihtokauppoihin vaikuttavien parametrien säätöä luokittelijan koulutuksen jälkeen, sekä puun rakenteen muodostamiseen liittyviä kysymyksiä

Perceived Nature - How Nature Is Presented On Film

How is nature presented on film? Many people only know about nature and wildlife from the documentaries they watch on television. As the urban city-dwelling audience disconnects from their own lived experience of nature and the outdoors, their awareness becomes limited to the framed image presented on film. The producers seek to capture a larger audience and increase sales, and often even the filmmakers choose to manipulate the content of their work in ways the audience is typically unable to detect. Deceiving the audience gives them an inaccurate and misconceived perception of reality. The thesis looks at the history of nature on film and the early deceptions some of these early films contained, discusses the techniques of audio and video manipulation, and compares modern examples of natural history films from Britain and the United States. In the qualitative-comparative analysis, two fairly recent series are compared for their representation of nature and effect on the audience; Planet Earth (2006) produced by the BBC, and Untamed Americas (2012) produced by the National Geographic Channel. The productions compared differed greatly in their portrayal of both animals and nature in general. The American production seemed to focus more on entertaining the viewer with scenes of action and descriptions of hardship, while the British production delivered more educational content, with narration based on well-researched facts rather than assumptions.Kuinka luonto esitetään filmillä? Monet perustavat tietämyksensä ja näkemyksensä luonnosta televisiosta katsomiinsa luontodokumentteihin, mutta minkälaisena nämä ohjelmat oikeastaan näyttävät luonnon? Samalla kun kaupungistuneet ihmiset erkanevat “oikeasta” luonnosta yhä enemmän, heidän käsityksensä perustuvat lähes yksinomaan näihin videomuodossa näytettyihin tuotantoihin.Kuten muissakin elokuvissa ja televisiotuotannoissa tänä päivänä, päätavoite myös luontodokumenteissa on loppujen lopuksi voittojen tuottaminen, eivätkä kaikki elokuvantekijät ole aina täysin vilpittömiä tuottaessaan materiaalia näihin ohjelmiin. Filmimateriaalin manipulointi ja lavastaminen ovat arkipäivää myös luontoa käsittelevissä sarjoissa, eivätkä katsojat voi olla asiasta mitenkään tietoisia. Muun muassa ylidramatisoidut kohtaukset antavat yleisölle vääristyneen kuvan luonnosta, jolla taas on omat seurauksensa esimerkiksi väestön suhtautumisessa villieläimiin. Opinnäytetyö selventää luonto-ohjelmien historiaa sekä näissä esiintyneitä lavastuksia ja muita manipulaatioita, kertoo tavoista joilla yleisöä “huijataan” myös nykypäivänä, sekä vertailee kahta viime vuosina tuotettua sarjaa Britanniasta ja Yhdysvalloista. Vertailukohteina olivat BBC-tuotanto Planet Earth (2006) sekä National Geographic Channelin tuottama Untamed Americas (2012). Vertaillut sarjat poikkesivat suuresti toisistaan tavassa, jolla luonto ja villieläimet tuotiin esille; Amerikkalainen esimerkki luotti nopeisiin toimintakohtauksiin ja keskittyi lähinnä yleisön viihdyttämiseen, syventymättä sen enempää “tylsiin” asioihin tai ympäristöön; kun taas brittiesimerkissä oli enemmän tutkittua tietoa eläimistä ja näiden elinympäristöstä, esitettynä tavalla joka ei juurikaan demonisoinut esimerkiksi petoeläimiä