Facultad Regional San Francisco

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Author: search.filters.author.Redolfi, JavierSubject: search.filters.subject.image classification

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    Exponential family Fisher vector for image classification
    (2015-07-01) Sánchez, Jorge; Redolfi, Javier
    One of the fundamental problems in image classification is to devise models that allow us to relate the images to higher-level semantic concepts in an efficient and reliable way. A widely used approach consists on extracting local descriptors from the images and to summarize them into an image-level representation. Within this framework, the Fisher vector (FV) is one of the most robust signatures to date. In the FV, local descriptors are modeled as samples drawn from a mixture of Gaussian pdfs. An image is represented by a gradient vector characterizing the distributions of samples w.r.t. the model. Equipped with robust features like SIFT, the FV has shown state-of-the-art performance on different recognition problems. However, it is not clear how it should be applied when the feature space is clearly non-Euclidean, leading to heuristics that ignore the underlying structure of the space. In this paper we generalize the Gaussian FV to a broader family of distributions known as the exponential family. The model, termed exponential family Fisher vectors (eFV), provides a unified framework from which rich and powerful representations can be derived. Experimental results show the generality and flexibility of our approach.
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    Fisher vectors for leaf image classification: an experimental evaluation
    (Springer, Cham, 2015-10-25) Redolfi, Javier; Sánchez, Jorge; Pucheta, Julián
    In this work we present an experimental evaluation of the exponential family Fisher vector (eFV) encoding applied to the problem of visual plant identification. We evaluate the performance of this model together with a variety of local image descriptors on four different datasets and compare the results with other methods proposed in the literature. Experiments show that the eFV achieves a performance that compares favorably with other state-of-the-art approaches on this problem.
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    Fisher Vectors for PolSAR Image Classification
    (SADIO, 2018-09) Redolfi, Javier; Sánchez, Jorge; Flesia, Ana Georgina
    In this letter, we study the application of the Fisher vector (FV) to the problem of pixelwise supervised classification of polarimetric synthetic aperture radar images. This is a challenging problem since information in those images is encoded as complex-valued covariance matrices. We observe that the real parts of these matrices preserve the positive semidefiniteness property of their complex counterpart. Based on this observation, we derive an FV from a mixture of real Wishart densities and integrate it with a Potts-like energy model in order to capture spatial dependencies between neighboring regions. Experimental results on two challenging data sets show the effectiveness of the approach.