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  • 1.
    Caputo, Barbara
    KTH, Superseded Departments, Numerical Analysis and Computer Science, NADA.
    A new kernel method for object recognition:spin glass-Markov random fields2004Doctoral thesis, monograph (Other scientific)
    Abstract [en]

    Recognizing objects through vision is an important part of our lives: we recognize people when we talk to them, we recognize our cup on the breakfast table, our car in a parking lot, and so on. While this task is performed with great accuracy and apparently little effort by humans, it is still unclear how this performance is achieved. Creating computer methods for automatic object recognition gives rise to challenging theoretical problems such as how to model the visual appearance of the objects or categories we want to recognize, so that the resulting algorithm will perform robustly in realistic scenarios; to this end, how to use effectively multiple cues (such as shape, color, textural properties and many others), so that the algorithm uses uses the best subset of cues in the most effective manner; how to use specific features and/or specific strategies for different classes.

    The present work is devoted to the above issues. We propose to model the visual appearance of objects and visual categories via probability density functions. The model is developed on the basis of concepts and results obtained in three different research areas: computer vision, machine learning and statistical physics of spin glasses. It consists of a fully connected Markov random field with energy function derived from results of statistical physics of spin glasses. Markov random fields and spin glass energy functions are combined together via nonlinear kernel functions; we call the model Spin Glass-Markov Random Fields. Full connectivity enables to take into account the global appearance of the object, and its specific local characteristics at the same time, resulting in robustness to noise, occlusions and cluttered background. Because of properties of some classes of spin glasslike energy functions, our model allows to use easily and effectively multiple cues, and to employ class specific strategies. We show with theoretical analysis and experiments that this new model is competitive with state-of-the-art algorithms for object recognition.

  • 2.
    Caputo, Barbara
    et al.
    KTH, School of Computer Science and Communication (CSC), Computer Vision and Active Perception, CVAP.
    Hayman, Eric
    KTH, School of Computer Science and Communication (CSC), Computer Vision and Active Perception, CVAP.
    Mallikarjuna, P.
    Class-specific material categorisation2005In: TENTH IEEE INTERNATIONAL CONFERENCE ON COMPUTER VISION, VOLS 1 AND 2, PROCEEDINGS, 2005, p. 1597-1604Conference paper (Refereed)
    Abstract [en]

    Although a considerable amount of work has been published on material classification, relatively little of it studies situations with considerable variation within each class. Many experiments use the exact same sample, or different patches front the same image, for training and test sets. Thus, such studies are vulnerable to effectively recognising one particular sample of a material as opposed to the material category. In contrast, this paper places firm emphasis on the capability to generalise to previously, unseen instances of materials. We adopt an appearance-based strategy, and conduct experiments on a new database which contains several samples of each of eleven material categories, imaged under a variety of pose, illumination and scale conditions. Together these sources of intra-class variation provide a stern challenge indeed for recognition. Somewhat surprisingly, the difference in performance between various state-of-the-art texture descriptors proves rather small in this task. On the other hand, we clearly demonstrate that very significant gains can be achieved via different SVM-based classification techniques. Selecting appropriate kernel parameters proves crucial. This motivates a novel recognition scheme based on a decision tree. Each node contains an SVM to split one class front all others with a kernel parameter optimal for that particular node. Hence, each decision is made using a different, optimal., class-specific metric. Experiments show the superiority of this approach over several state-of-the-art classifiers.

  • 3.
    Caputo, Barbara
    et al.
    KTH, Superseded Departments, Numerical Analysis and Computer Science, NADA.
    Wallraven, Christian
    Nilsback, Maria-Elena
    KTH, Superseded Departments, Numerical Analysis and Computer Science, NADA.
    Object categorization via local kernels2004In: PROCEEDINGS OF THE 17TH INTERNATIONAL CONFERENCE ON PATTERN RECOGNITION, VOL 2 / [ed] Kittler, J; Petrou, M; Nixon, M, 2004, p. 132-135Conference paper (Refereed)
    Abstract [en]

    This paper considers the problem of multi-object categorization. We present an algorithm that combines support vector machines with local features via a new class of Mercer kernels. This class of kernels allows us to perform scalar products on feature vectors consisting of local descriptors, computed around interest points (like corners); these feature vectors are generally of different lengths for different images. The resulting framework is able to recognize multi-object categories in different settings, from lab-controlled to real-world scenes. We present several experiments, on different databases, and we benchmark our results with state-of-the-art algorithms for categorization, achieving excellent results.

  • 4. Fritz, M.
    et al.
    Leibe, B.
    Caputo, Barbara
    KTH, School of Computer Science and Communication (CSC), Computer Vision and Active Perception, CVAP.
    Schiele, B.
    Integrating representative and discriminant models for object category detection2005In: Tenth IEEE International Conference on  (Volume:2 ) Computer Vision, 2005. ICCV 2005, IEEE Computer Society, 2005, p. 1363-1370Conference paper (Refereed)
    Abstract [en]

    Category detection is a lively area of research. While categorization algorithms tend to agree in using local descriptors, they differ in the choice of the classifier, with some using generative models and others discriminative approaches. This paper presents a method for object category detection which integrates a generative model with a discriminative classifier. For each object category, we generate an appearance codebook, which becomes a common vocabulary for the generative and discriminative methods. Given a query image, the generative part of the algorithm finds a set of hypotheses and estimates their support in location and scale. Then, the discriminative part verifies each hypothesis on the same codebook activations. The new algorithm exploits the strengths of both original methods, minimizing their weaknesses. Experiments on several databases show that our new approach performs better than its building blocks taken separately. Moreover, experiments on two challenging multi-scale databases show that our new algorithm outperforms previously reported results.

  • 5.
    Hayman, Eric
    et al.
    KTH, Superseded Departments, Numerical Analysis and Computer Science, NADA.
    Caputo, Barbara
    KTH, Superseded Departments, Numerical Analysis and Computer Science, NADA.
    Fritz, Mario
    KTH, Superseded Departments, Numerical Analysis and Computer Science, NADA.
    Eklundh, Jan Olof
    KTH, Superseded Departments, Numerical Analysis and Computer Science, NADA.
    On the significance of real-world conditions for material classification2004In: COMPUTER VISION - ECCV 2004, PT 4, BERLIN: SPRINGER , 2004, Vol. 2034, p. 253-266Conference paper (Refereed)
    Abstract [en]

    Classifying materials from their appearance is a challenging problem, highlights especially if illumination and pose conditions are permitted to change: and shadows caused by 3D structure can radically alter a sample's visual texture. Despite these difficulties, researchers have demonstrated impressive results on the CUReT database which contains many images of 61 materials under different conditions. A first contribution of this paper is to further advance the state-of-the-art by applying Support Vector Machines to this problem. To our knowledge, we record the best results to date on the CUReT database. In our work we additionally investigate the effect of scale since robustness to viewing distance and zoom settings is crucial in many real-world situations. Indeed, a material's appearance can vary considerably as fine-level detail becomes visible or disappears as the camera moves towards or away from the subject. We handle scale- variations using a pure-learning approach, incorporating samples imaged at different distances into the training set. An empirical investigation is conducted to show how the classification accuracy decreases as less scale information is made available during training. Since the CUReT database contains little scale variation, we introduce a new database which images ten CUReT materials at different distances, while also maintaining some change in pose and illumination. The first aim of the database is thus to provide scale variations, but a second and equally important objective is to attempt to recognise different samples of the CUReT materials. For instance, does training on the CUReT database enable recognition of anotherpiece of sandpaper? The results clearly demonstrate that it is not possible to do so with any acceptable degree of accuracy. Thus we conclude that impressive results even on a well-designed database such as CUReT, does not imply that material classification is close to being a solved problem under real-world conditions.

  • 6. La Torre, E.
    et al.
    Tommasi, T.
    Caputo, Barbara
    KTH, School of Computer Science and Communication (CSC), Computer Vision and Active Perception, CVAP.
    Gigante, G. E.
    Kernel methods for melanoma recognition2006In: Stud. Health Technol. Informatics, 2006, p. 983-988Conference paper (Refereed)
    Abstract [en]

    Skin cancer is a spreading disease in the western world. Early detection and treatment are crucial for improving the patient survival rate. In this paper we present two algorithms for computer assisted diagnosis of melanomas. The first is the support vector machines algorithm, a state-of-the-art large margin classifier, which has shown remarkable performances on object recognition and categorization problems. The second method, spin glass-Markov random fields, combines results of statistical physics of spin glasses with Markov random fields. We compared the two approaches using color histograms as features. We benchmarked our methods with another algorithm presented in the literature, which uses a sophisticated segmentation technique and a set of features especially designed for melanoma recognition. To our knowledge, this algorithm represents the state of the art on skin lesions classification. We show with extensive experiments that the support vector machines approach outperforms the existing method and, on two classes out of three, it achieves performances comparable to those obtained by expert clinicians.

  • 7.
    Luo, Jie
    et al.
    KTH, School of Computer Science and Communication (CSC), Computer Vision and Active Perception, CVAP.
    Pronobis, Andrzej
    KTH, School of Computer Science and Communication (CSC), Computer Vision and Active Perception, CVAP.
    Caputo, Barbara
    KTH, School of Computer Science and Communication (CSC), Computer Vision and Active Perception, CVAP.
    SVM-based Transfer of Visual Knowledge Across Robotic Platforms2007In: Proceedings of the 5th International Conference on Computer Vision Systems (ICVS’07), Applied Computer Science Group, Bielefeld University, Germany , 2007Conference paper (Refereed)
    Abstract [en]

    This paper presents an SVM-based algorithm for the transfer of knowledge across robot platforms aiming to perform the same task. Our method exploits efficiently the transferred knowledge while updating incrementally the internal representation as new information is available. The algorithm is adaptive and tends to privilege new data when building the SV solution. This prevents the old knowledge to nest into the model and eventually become a possible source of misleading information. We tested our approach in the domain of vision-based place recognition. Extensive experiments show that using transferred knowledge clearly pays off in terms of performance and stability of the solution.

  • 8.
    Nilsback, Maria Elena
    et al.
    KTH, Superseded Departments, Numerical Analysis and Computer Science, NADA.
    Caputo, Barbara
    KTH, Superseded Departments, Numerical Analysis and Computer Science, NADA.
    Cue integration through discriminative accumulation2004In: PROCEEDINGS OF THE 2004 IEEE COMPUTER SOCIETY CONFERENCE ON COMPUTER VISION AND PATTERN RECOGNITION, VOL 2, 2004, p. 578-585Conference paper (Refereed)
    Abstract [en]

    Object recognition systems aiming to work in real world settings should use multiple cues in order to achieve robustness. We present a new cue integration scheme which extends the idea of cue accumulation to discriminative classifiers. We derive and test the scheme for Support Vector Machines (SVMs), but we also show that it is easily extendible to any large margin classifier Interestingly, in the case of one-class SVMs, the scheme can be interpreted as a new class of Mercer kernels for multiple cues. Experimental comparison with a probabilistic accumulation scheme is favorable to our method. Comparison with voting scheme shows that our method may suffer as the number of object classes increases. Based on these results, we propose a recognition algorithm consisting of a decision tree where decisions at each node are taken using our accumulation scheme. Results obtained using this new algorithm compare very favorably to accumulation (both probabilistic and discriminative) and voting scheme.

  • 9.
    Pronobis, Andrzej
    et al.
    KTH, School of Computer Science and Communication (CSC), Centres, Centre for Autonomous Systems, CAS. KTH, School of Computer Science and Communication (CSC), Computer Vision and Active Perception, CVAP.
    Caputo, Barbara
    COLD: The CoSy Localization Database2009In: The international journal of robotics research, ISSN 0278-3649, E-ISSN 1741-3176, Vol. 28, no 5, p. 588-594Article in journal (Refereed)
    Abstract [en]

    Two key competencies for mobile robotic systems are localization and semantic context interpretation. Recently, vision has become the modality of choice for these problems as it provides richer and more descriptive sensory input. At the same time, designing and testing vision-based algorithms still remains a challenge, as large amounts of carefully selected data are required to address the high variability of visual information. In this paper we present a freely available database which provides a large-scale, flexible testing environment for vision-based topological localization and semantic knowledge extraction in robotic systems. The database contains 76 image sequences acquired in three different indoor environments across Europe. Acquisition was performed with the same perspective and omnidirectional camera setup, in rooms of different functionality and under various conditions. The database is an ideal testbed for evaluating algorithms in real-world scenarios with respect to both dynamic and categorical variations.

  • 10.
    Pronobis, Andrzej
    et al.
    KTH, School of Computer Science and Communication (CSC), Computer Vision and Active Perception, CVAP. KTH, School of Computer Science and Communication (CSC), Centres, Centre for Autonomous Systems, CAS.
    Caputo, Barbara
    KTH, School of Computer Science and Communication (CSC), Centres, Centre for Autonomous Systems, CAS.
    Confidence-based cue integration for visual place recognition2007In: 2007 IEEE/RSJ INTERNATIONAL CONFERENCE ON INTELLIGENT ROBOTS AND SYSTEMS, VOLS 1-9, NEW YORK: IEEE , 2007, p. 2400-2407Conference paper (Refereed)
    Abstract [en]

    A distinctive feature of intelligent systems is their capability to analyze their level of expertise for a given task; in other words, they know what they know. As a way towards this ambitious goal, this paper presents a recognition algorithm able to measure its own level of confidence and, in case of uncertainty, to seek for extra information so to increase its own knowledge and ultimately achieve better performance. We focus on the visual place recognition problem for topological localization, and we take an SVM approach. We propose a new method for measuring the confidence level of the classification output, based on the distance of a test image and the average distance of training vectors. This method is combined with a discriminative accumulation scheme for cue integration. We show with extensive experiments that the resulting algorithm achieves better performances for two visual cues than the classic single cue SVM on the same task, while minimising the computational load. More important, our method provides a reliable measure of the level of confidence of the decision.

  • 11.
    Pronobis, Andrzej
    et al.
    KTH, School of Computer Science and Communication (CSC), Centres, Centre for Autonomous Systems, CAS. KTH, School of Computer Science and Communication (CSC), Computer Vision and Active Perception, CVAP.
    Caputo, Barbara
    KTH, School of Computer Science and Communication (CSC), Centres, Centre for Autonomous Systems, CAS. KTH, School of Computer Science and Communication (CSC), Computer Vision and Active Perception, CVAP.
    Jensfelt, Patric
    KTH, School of Computer Science and Communication (CSC), Centres, Centre for Autonomous Systems, CAS. KTH, School of Computer Science and Communication (CSC), Computer Vision and Active Perception, CVAP.
    Christensen, Henrik I.
    KTH, School of Computer Science and Communication (CSC), Centres, Centre for Autonomous Systems, CAS. KTH, School of Computer Science and Communication (CSC), Computer Vision and Active Perception, CVAP.
    A discriminative approach to robust visual place recognition2006In: 2006 IEEE/RSJ International Conference on Intelligent Robots and Systems, Vols 1-12, NEW YORK: IEEE , 2006, p. 3829-3836Conference paper (Refereed)
    Abstract [en]

    An important competence for a mobile robot system is the ability to localize and perform context interpretation. This is required to perform basic navigation and to facilitate local specific services. Usually localization is performed based on a purely geometric model. Through use of vision and place recognition a number of opportunities open up in terms of flexibility and association of semantics to the model. To achieve this the present paper presents an appearance based method for place recognition. The method is based on a large margin classifier in combination with a rich global image descriptor. The method is robust to variations in illumination and minor scene changes. The method is evaluated across several different cameras, changes in time-of-day and weather conditions. The results clearly demonstrate the value of the approach.

  • 12.
    Schüldt, Christian
    et al.
    KTH, Superseded Departments, Numerical Analysis and Computer Science, NADA.
    Laptev, Ivan
    KTH, Superseded Departments, Numerical Analysis and Computer Science, NADA.
    Caputo, Barbara
    KTH, Superseded Departments, Numerical Analysis and Computer Science, NADA.
    Recognizing human actions: A local SVM approach2004In: PROCEEDINGS OF THE 17TH INTERNATIONAL CONFERENCE ON PATTERN RECOGNITION, VOL 3 / [ed] Kittler, J; Petrou, M; Nixon, M, 2004, p. 32-36Conference paper (Refereed)
    Abstract [en]

    Local space-time features capture local events in video and can be adapted to the size, the frequency and the velocity of moving patterns. In this paper we demonstrate how such features can be used for recognizing complex motion patterns. We construct video representations in terms of local space-time features and integrate such representations with SVM classification schemes for recognition. For the purpose of evaluation we introduce a new video database containing 2391 sequences of six human actions performed by 25 people in four different scenarios. The presented results of action recognition justify the proposed method and demonstrate its advantage compared to other relative approaches for action recognition.

  • 13. Tommasi, Tatiana
    et al.
    La Torre, Elisabetta
    Caputo, Barbara
    KTH, School of Computer Science and Communication (CSC), Computer Vision and Active Perception, CVAP.
    Melanoma recognition using representative and discriminative kernel classifiers2006In: Computer Vision Approaches To Medical Image Analysis / [ed] Beichel, RR, 2006, Vol. 4241, p. 1-12Conference paper (Refereed)
    Abstract [en]

    Malignant melanoma is the most deadly form of skin lesion. Early diagnosis is of critical importance to patient survival. Existent visual recognition algorithms for skin lesions classification focus mostly on segmentation and feature extraction. In this paper instead we put the emphasis on the learning process by using two kernel-based classifiers. We chose a discriminative approach using support vector machines, and a probabilistic approach using spin glass-Markov random fields. We benchmarked these algorithms against the (to our knowledge) state-of-the-art method on melanoma recognition, exploring how performance changes by using color or textural features, and how it is affected by the quality of the segmentation mask. We show with extensive experiments that the support vector machine approach outperforms the existing method and, on two classes out of three, it achieves performances comparable to those obtained by expert clinicians.

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