Performance characterization of clustering algorithms for colour image segmentation

This paper details the implementation of three traditional clustering techniques (K-Means clustering, Fuzzy C-Means clustering and Adaptive K-Means clustering) that are applied to extract the colour information that is used in the image segmentation process. The aim of this paper is to evaluate the performance of the analysed colour clustering techniques for the extraction of optimal features from colour spaces and investigate which method returns the most consistent results when applied on a large suite of mosaic images

Color image segmentation using a spatial k-means clustering algorithm

This paper details the implementation of a new adaptive technique for color-texture segmentation that is a generalization of the standard K-Means algorithm. The standard K-Means algorithm produces accurate segmentation results only when applied to images defined by homogenous regions with respect to texture and color since no local constraints are applied to impose spatial continuity. In addition, the initialization of the K-Means algorithm is problematic and usually the initial cluster centers are randomly picked. In this paper we detail the implementation of a novel technique to select the dominant colors from the input image using the information from the color histograms. The main contribution of this work is the generalization of the K-Means algorithm that includes the primary features that describe the color smoothness and texture complexity in the process of pixel assignment. The resulting color segmentation scheme has been applied to a large number of natural images and the experimental data indicates the robustness of the new developed segmentation algorithm

Clusters of firms in space and time

The use of the K-functions (Ripley, 1977) has become recently popular in the analysis of the spatial pattern of firms. It was first introduced in the economic literature by Arbia and Espa (1996) and then popularized by Marcon and Puech (2003), Quah and Simpson (2003), Duranton and Overman (2005) and Arbia et al. (2008). In particular in Arbia et al. (2008) we used Ripley’s K-functions as instruments to study the inter-sectoral co-agglomeration pattern of firms in a single moment of time. All this researches have followed a static approach, disregarding the time dimension. Temporal dynamics, on the other hand, play a crucial role in understanding the economic and social phenomena, particularly when referring to the analysis of the individual choices leading to the observed clusters of economic activities. With respect to the contributions previously appeared in the literature, this paper uncovers the process of firm demography by studying the dynamics of localization through space-time K-functions. The empirical part of the paper will focus on the study of the long run localization of firms in the area of Rome (Italy), by concentrating on the ICT sector data collected by the Italian Industrial Union in the period 1920- 2005.Agglomeration, Non-parametric measures; Space-time K-functions, Spatial clusters, Spatial econometrics.

MAPPING LOCAL PRODUCTIVITY ADVANTAGES IN ITALY: INDUSTRIAL DISTRICTS, CITIES OR BOTH?

In this paper we compare the magnitude of local productivity advantages associated to two different spatial concentration patterns in Italy, i.e. urban areas (UA) and industrial districts (ID). UA typically display a huge concentration of population and host a wide range of economic activities, while ID are located outside UA and exhibit a strong concentration of small firms producing relatively homogenous goods. We use a very large sample of Italian manufacturing firms observed over the 1995-2006 period and resort to a wide set of econometric techniques in order to test the robustness of main empirical findings. We detect local productivity advantages for both UA and ID. However, firms located in UA attain a larger Total Factor Productivity (TFP) premium than those operating within ID. Besides, it turns out that the advantages of ID have declined over time, while those of UA remained stable. Differences in the white-blue collars composition of the local labor force appear to explain only a minor fraction of the estimated spatial TFP differentials. Production workers (blue collars) turn out to be more productive in ID, while non-production workers (white collars) are more efficiently employed in UA. By analyzing the quantiles of the sample TFP distribution, we document how higher average TFP levels within UA do not seem to be mainly driven by a selection effect pushing less efficient firms out of the market. Rather, a firm sorting effect appears to stand out, suggesting that more productive firms gain strong benefits from locating in UA. On the whole, our analysis raises the question whether Italian ID are less fit than UA to prosper in a changing world, characterized by increased globalization and by the growing use of information technologies.

Searching for clusters in tourism. A quantitative methodological proposal

The tourism industry is one of Europe’s leading employers, and for many regions highly dependent on tourists’ spending, innovation is the difference between growth and stagnation. Thus, at a regional level, tourism may function as a driving force of socioeconomic development and thus contribute to the demise of regional disparities. Such lever effect is usually associated to a geographical concentration abusively denominated of clusters. Most of the studies within the tourism industry identify clusters resorting to simplistic analyses of geographic location measures or experts’ opinions. These latter tend to neglect the essence of the cluster concept, namely the inter-linkages among regional actors. In the present paper, we propose a methodology to rigorously identify tourism clusters, stressing the importance of networks and cooperation between agents.Clusters; Tourism; Methodology

Adaptive constrained clustering with application to dynamic image database categorization and visualization.

The advent of larger storage spaces, affordable digital capturing devices, and an ever growing online community dedicated to sharing images has created a great need for efficient analysis methods. In fact, analyzing images for the purpose of automatic categorization and retrieval is quickly becoming an overwhelming task even for the casual user. Initially, systems designed for these applications relied on contextual information associated with images. However, it was realized that this approach does not scale to very large data sets and can be subjective. Then researchers proposed methods relying on the content of the images. This approach has also proved to be limited due to the semantic gap between the low-level representation of the image and the high-level user perception. In this dissertation, we introduce a novel clustering technique that is designed to combine multiple forms of information in order to overcome the disadvantages observed while using a single information domain. Our proposed approach, called Adaptive Constrained Clustering (ACC), is a robust, dynamic, and semi-supervised algorithm. It is based on minimizing a single objective function incorporating the abilities to: (i) use multiple feature subsets while learning cluster independent feature relevance weights; (ii) search for the optimal number of clusters; and (iii) incorporate partial supervision in the form of pairwise constraints. The content of the images is used to extract the features used in the clustering process. The context information is used in constructing a set of appropriate constraints. These constraints are used as partial supervision information to guide the clustering process. The ACC algorithm is dynamic in the sense that the number of categories are allowed to expand and contract depending on the distribution of the data and the available set of constraints. We show that the proposed ACC algorithm is able to partition a given data set into meaningful clusters using an adaptive, soft constraint satisfaction methodology for the purpose of automatically categorizing and summarizing an image database. We show that the ACC algorithm has the ability to incorporate various types of contextual information. This contextual information includes: spatial information provided by geo-referenced images that include GPS coordinates pinpointing their location, temporal information provided by each image\u27s time stamp indicating the capture time, and textual information provided by a set of keywords describing the semantics of the associated images

A generic framework for context-dependent fusion with application to landmine detection.

For complex detection and classification problems, involving data with large intra-class variations and noisy inputs, no single source of information can provide a satisfactory solution. As a result, combination of multiple classifiers is playing an increasing role in solving these complex pattern recognition problems, and has proven to be a viable alternative to using a single classifier. Over the past few years, a variety of schemes have been proposed for combining multiple classifiers. Most of these were global as they assign a degree of worthiness to each classifier, that is averaged over the entire training data. This may not be the optimal way to combine the different experts since the behavior of each one may not be uniform over the different regions of the feature space. To overcome this issue, few local methods have been proposed in the last few years. Local fusion methods aim to adapt the classifiers\u27 worthiness to different regions of the feature space. First, they partition the input samples. Then, they identify the best classifier for each partition and designate it as the expert for that partition. Unfortunately, current local methods are either computationally expensive and/or perform these two tasks independently of each other. However, feature space partition and algorithm selection are not independent and their optimization should be simultaneous. In this dissertation, we introduce a new local fusion approach, called Context Extraction for Local Fusion (CELF). CELF was designed to adapt the fusion to different regions of the feature space. It takes advantage of the strength of the different experts and overcome their limitations. First, we describe the baseline CELF algorithm. We formulate a novel objective function that combines context identification and multi-algorithm fusion criteria into a joint objective function. The context identification component thrives to partition the input feature space into different clusters (called contexts), while the fusion component thrives to learn the optimal fusion parameters within each cluster. Second, we propose several variations of CELF to deal with different applications scenario. In particular, we propose an extension that includes a feature discrimination component (CELF-FD). This version is advantageous when dealing with high dimensional feature spaces and/or when the number of features extracted by the individual algorithms varies significantly. CELF-CA is another extension of CELF that adds a regularization term to the objective function to introduce competition among the clusters and to find the optimal number of clusters in an unsupervised way. CELF-CA starts by partitioning the data into a large number of small clusters. As the algorithm progresses, adjacent clusters compete for data points, and clusters that lose the competition gradually become depleted and vanish. Third, we propose CELF-M that generalizes CELF to support multiple classes data sets. The baseline CELF and its extensions were formulated to use linear aggregation to combine the output of the different algorithms within each context. For some applications, this can be too restrictive and non-linear fusion may be needed. To address this potential drawback, we propose two other variations of CELF that use non-linear aggregation. The first one is based on Neural Networks (CELF-NN) and the second one is based on Fuzzy Integrals (CELF-FI). The latter one has the desirable property of assigning weights to subsets of classifiers to take into account the interaction between them. To test a new signature using CELF (or its variants), each algorithm would extract its set of features and assigns a confidence value. Then, the features are used to identify the best context, and the fusion parameters of this context are used to fuse the individual confidence values. For each variation of CELF, we formulate an objective function, derive the necessary conditions to optimize it, and construct an iterative algorithm. Then we use examples to illustrate the behavior of the algorithm, compare it to global fusion, and highlight its advantages. We apply our proposed fusion methods to the problem of landmine detection. We use data collected using Ground Penetration Radar (GPR) and Wideband Electro -Magnetic Induction (WEMI) sensors. We show that CELF (and its variants) can identify meaningful and coherent contexts (e.g. mines of same type, mines buried at the same site, etc.) and that different expert algorithms can be identified for the different contexts. In addition to the land mine detection application, we apply our approaches to semantic video indexing, image database categorization, and phoneme recognition. In all applications, we compare the performance of CELF with standard fusion methods, and show that our approach outperforms all these methods

Essays in Urban Economics

As developed economies have shifted from producing manufacturing goods, to been producers of knowledge and innovation, the traditional drivers of economic growth are giving way to an economy based on skills and creativity. As a result, the aptitudes and knowledge of the workforce have become important drivers for economic growth. The importance of geography and the forces of agglomeration in determining the location of human capital will keep growing, and cities with a large percentage of highly skilled workers will become the focal points for this transformation. This new industrial revolution has exacerbated regional disparities by an order of magnitude. Cities are diverging not only in their housing prices and productivity, but also in the skill composition of their workforce. Inequality has not only increase between regions but important changes have also taken place within regions, as large cities have become more unequal than the countries that host them.In this context it is more important than ever to understand how cities operate, and what drives the value of proximity in a knowledge economy. This is complicated since the uneven distribution of economic activity in space is partly driven by endogenous interactions between firms and workers in goods and factor markets which can move between regions. In Chapter I, titled "On the Geography of Inequality: Labor Sorting and Place-Based Policies in General Equilibrium", I study how city fundamentals, like amenities and housing restrictions, contribute to aggregate wage inequality through the sorting of heterogeneously skilled workers. I develop a ``system of cities'' model that features workers who differ along a continuum of skills and who compete for limited housing. This model is quantitatively tractable, and can replicate patterns in the dispersion of wages and housing prices both between and within cities. I calibrate this model to match different moments of the distributions of talent and wages for a cross-section of US cities, and I use it to understand the importance of sorting when accounting for patterns of regional inequality. I then evaluate the general equilibrium effects of an important place-based policy, namely housing policy, and find that a 1\% expansion in the supply of houses in more constrained cities can improve aggregate productivity between 0.2\% and 0.4\%. These effects would be larger in the absence of sorting. Moreover, relaxing housing constraints in those cities also tends to increase aggregate wage inequality.In Chapter II, titled "Urban Connectivity", I study how technological changes that affect urban connectivity (the efficiency with which workers use their productive time in a city) can explain the increased spatial segregation in workers' skills and firms' productivity. I focus in an economy that produces knowledge and requires the matching of heterogeneous firms and workers. I provide a spatial equilibrium model that has the unique feature that allows for the sorting of a continuum of firms and workers where productive complementarities are city specific. I show that small changes in the connectivity of a city, can generate non-linear changes in city sizes and the level of skill segregation between cities. This suggest that small shocks to the productive environment of a city could account for the important changes we have observed in workers' skills and firms' productivity distributions.Finally, in Chapter III, title “Clustering to Coordinate: Who Benefits From Knowledge Spillovers?”(joint work with William Grieser and Gonzalo Maturana), we study location and investment decisions by firms. Firm clustering is positively correlated with productivity, and it exhibits significant cross-sectional variation across industries. Thus, it is important to understand the industry characteristics that drive firms' decisions to co-locate. We develop a model of knowledge sharing and derive the prediction that riskier and more complex industries experience the greatest gains from knowledge spillovers. Using tests that account for the non-randomness of location decisions, we find a strong positive relationship between industry risk or complexity and the clustering of: 1) firms' headquarters, 2) patent inventors, and 3) R\&D expenses. Customer--supplier proximity is also significantly and positively related to industry risk and complexity