83,813 research outputs found

    Mid-level Deep Pattern Mining

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    Mid-level visual element discovery aims to find clusters of image patches that are both representative and discriminative. In this work, we study this problem from the prospective of pattern mining while relying on the recently popularized Convolutional Neural Networks (CNNs). Specifically, we find that for an image patch, activations extracted from the first fully-connected layer of CNNs have two appealing properties which enable its seamless integration with pattern mining. Patterns are then discovered from a large number of CNN activations of image patches through the well-known association rule mining. When we retrieve and visualize image patches with the same pattern, surprisingly, they are not only visually similar but also semantically consistent. We apply our approach to scene and object classification tasks, and demonstrate that our approach outperforms all previous works on mid-level visual element discovery by a sizeable margin with far fewer elements being used. Our approach also outperforms or matches recent works using CNN for these tasks. Source code of the complete system is available online.Comment: Published in Proc. IEEE Conf. Computer Vision and Pattern Recognition 201

    Learning from the Wisdom of The Prophets: Spiritual Intelligence of HÅ«d and Muįø„ammad in Ibn Arabiā€™s View

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    The wisdom of the prophets in Ibn ā€˜Arabiā€™s Fuį¹£Å«į¹£ al-Hikam is deeply concerned with discovering how the prophets who are taken up in each chapter exemplify different facets of the deeper spiritual process of the divine-human relation. This article examines two particular fass and wisdom of HÅ«d and Muhammad. The wisdom of Hud represents knowledge through the feetā€ (ilm al-rijl), the knowledge that can only come through actually traveling through all the tests and lessons of the earthly human existence or sulÅ«k, while the wisdom of Muhammad defines the role of love and its multiple layers. Both are seen to be a spiritual intelligence of the prophets. Spiritual Intelligence empowers people to deal with and resolve life-world issues while demonstrating virtuous behavior such as humility, compassion, gratitude, and wisdom. For Ibn ā€˜ArabÄ«, spiritual intelligence is about discovering intrinsic distinctions between truth and illusion, and spiritual discernment is all about. Finally, through his particular work, Ibn ā€˜ArabÄ« highlights and assumes a recurrent progression from habitual conditioning that humans usually encounter to a greater depth and breadth of consciousness

    No Spare Parts: Sharing Part Detectors for Image Categorization

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    This work aims for image categorization using a representation of distinctive parts. Different from existing part-based work, we argue that parts are naturally shared between image categories and should be modeled as such. We motivate our approach with a quantitative and qualitative analysis by backtracking where selected parts come from. Our analysis shows that in addition to the category parts defining the class, the parts coming from the background context and parts from other image categories improve categorization performance. Part selection should not be done separately for each category, but instead be shared and optimized over all categories. To incorporate part sharing between categories, we present an algorithm based on AdaBoost to jointly optimize part sharing and selection, as well as fusion with the global image representation. We achieve results competitive to the state-of-the-art on object, scene, and action categories, further improving over deep convolutional neural networks

    Integrating modes of policy analysis and strategic management practice : requisite elements and dilemmas

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    There is a need to bring methods to bear on public problems that are inclusive, analytic, and quick. This paper describes the efforts of three pairs of academics working from three different though complementary theoretical foundations and intervention backgrounds (i.e., ways of working) who set out together to meet this challenge. Each of the three pairs had conducted dozens of interventions that had been regarded as successful or very successful by the client groups in dealing with complex policy and strategic problems. One approach focused on leadership issues and stakeholders, another on negotiating competitive strategic intent with attention to stakeholder responses, and the third on analysis of feedback ramifications in developing policies. This paper describes the 10 year longitudinal research project designed to address the above challenge. The important outcomes are reported: the requisite elements of a general integrated approach and the enduring puzzles and tensions that arose from seeking to design a wide-ranging multi-method approach

    Predictive User Modeling with Actionable Attributes

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    Different machine learning techniques have been proposed and used for modeling individual and group user needs, interests and preferences. In the traditional predictive modeling instances are described by observable variables, called attributes. The goal is to learn a model for predicting the target variable for unseen instances. For example, for marketing purposes a company consider profiling a new user based on her observed web browsing behavior, referral keywords or other relevant information. In many real world applications the values of some attributes are not only observable, but can be actively decided by a decision maker. Furthermore, in some of such applications the decision maker is interested not only to generate accurate predictions, but to maximize the probability of the desired outcome. For example, a direct marketing manager can choose which type of a special offer to send to a client (actionable attribute), hoping that the right choice will result in a positive response with a higher probability. We study how to learn to choose the value of an actionable attribute in order to maximize the probability of a desired outcome in predictive modeling. We emphasize that not all instances are equally sensitive to changes in actions. Accurate choice of an action is critical for those instances, which are on the borderline (e.g. users who do not have a strong opinion one way or the other). We formulate three supervised learning approaches for learning to select the value of an actionable attribute at an instance level. We also introduce a focused training procedure which puts more emphasis on the situations where varying the action is the most likely to take the effect. The proof of concept experimental validation on two real-world case studies in web analytics and e-learning domains highlights the potential of the proposed approaches
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