Yuzuguler, Ahmet Caner
Vural, Elif
Frossard, Pascal
Sparse representations of images in well-designed dictionaries can be used for effective classification. Meanwhile, training data available in most realistic settings are likely to be exposed to geometric transformations, which poses a challenge for the design of good dictionaries. In this work, we study the problem of learning class-representative dictionaries from geometrically transformed image sets. In order to efficiently take account of arbitrary geometric transformations in the learning, we adopt a representation of the dictionaries in an analytic basis. Then, the proposed algorithm learns atoms that are attracted to the samples of their own class while being repelled from the samples of other classes so that the discrimination between different classes is promoted. The dictionary learning objective is formulated such that it enhances the class-discrimination capabilities of individual atoms rather than the ones of the subspaces they generate, which renders the designed dictionaries especially suitable for fast classification of query images with very sparse approximations. Experimental results demonstrate the performance of the proposed method in handwritten digit recognition applications.


Multisource region attention network for fine-grained object recognition in remote sensing imagery
Sümbül, Gencer; Cinbiş, Ramazan Gökberk; Aksoy, Selim (Institute of Electrical and Electronics Engineers (IEEE), 2019-07)
Fine-grained object recognition concerns the identification of the type of an object among a large number of closely related subcategories. Multisource data analysis that aims to leverage the complementary spectral, spatial, and structural information embedded in different sources is a promising direction toward solving the fine-grained recognition problem that involves low between-class variance, small training set sizes for rare classes, and class imbalance. However, the common assumption of coregistered ...
Classification of hyperspectral images based on weighted DMPs
Ulusoy, İlkay; MURA, Mauro Dalla (2012-07-27)
This paper presents a classification method for hyperspectral images utilizing Differential Morphological Profiles (DMPs) which permit to include in the analysis spatial information since they can provide an estimate of the size and contrast characteristics of the structures in an image. Due to the wide variety of objects present in a scene, the pixels belonging to the same semantic structure may not have homogeneous spatial and spectral features. In addition, instead of a single peak (which can be related ...
Approximate Fisher Kernels of Non-iid Image Models for Image Categorization
Cinbiş, Ramazan Gökberk; Schmid, Cordelia (2016-06-01)
The bag-of-words (BoW) model treats images as sets of local descriptors and represents them by visual word histograms. The Fisher vector (FV) representation extends BoW, by considering the first and second order statistics of local descriptors. In both representations local descriptors are assumed to be identically and independently distributed (iid), which is a poor assumption from a modeling perspective. It has been experimentally observed that the performance of BoW and FV representations can be improved...
Learning pattern transformation manifolds for classification
Vural, Elif (2013-02-21)
Manifold models provide low-dimensional representations that are useful for analyzing and classifying data in a transformation-invariant way. In this paper we study the problem of jointly building multiple pattern transformation manifolds from a collection of image sets, where each set consists of observations from a class of geometrically transformed signals. We build the manifolds such that each manifold approximates a different signal class. Each manifold is characterized by a representative pattern that...
Weakly supervised instance attention for multisource fine-grained object recognition with an application to tree species classification
Aygunes, Bulut; Cinbiş, Ramazan Gökberk; Aksoy, Selim (2021-06-01)
Multisource image analysis that leverages complementary spectral, spatial, and structural information benefits fine-grained object recognition that aims to classify an object into one of many similar subcategories. However, for multisource tasks that involve relatively small objects, even the smallest registration errors can introduce high uncertainty in the classification process. We approach this problem from a weakly supervised learning perspective in which the input images correspond to larger neighborh...
Citation Formats
A. C. Yuzuguler, E. Vural, and P. Frossard, “TRANSFORMATION-INVARIANT DICTIONARY LEARNING FOR CLASSIFICATION WITH 1-SPARSE REPRESENTATIONS,” 2014, Accessed: 00, 2020. [Online]. Available: