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Appearance-based methods are mostly exploited in the recognition of specific objects, especially faces; while methods with local features are often applied to the recognition of generic objects. Only few works report the performance of appearance-based methods applied to generic object recognition. This paper offers a comparison study to extend our understanding in this regard. The appearance features...
We present an Online Random Ferns (ORFs) classifier that progressively learns and builds enhanced models of object appearances. During the learning process, we allow the human intervention to assist the classifier and discard false positive training samples. The amount of human intervention is minimized and integrated within the online learning, such that in a few seconds, complex object appearances...
This paper addresses the problem of Car Make and Model recognition as an example of within-category object class recognition. In this problem, it is assumed that the general category of the object is given and the goal is to recognize the object class within the same category. As compared to general object recognition, this problem is more challenging because the variations among classes within the...
A simple and effective method is proposed for object recognition via collaborative representation with ridge regression. Different from existing sparse representation and collaborative representation based approaches, the proposal does not need extensive training samples for each testing class and it is robust to localization errors and large within-class variations, thus being applicable to various...
Many Object recognition techniques perform some flavour of point pattern matching between a model and a scene. Such points are usually selected through a feature detection algorithm that is robust to a class of image transformations and a suitable descriptor is computed over them in order to get a reliable matching. Moreover, some approaches take an additional step by casting the correspondence problem...
In this study, we address the issue on multilevel object recognition. The multilevel object recognition is object recognition in various levels, that is, simultaneous recognition of its instance, category, material, etc. At each level, many recognition methods have been proposed in the literature. Therefore it is straightforward to design a multilevel object recognition system using conventional methods...
Visual cortex inspired features mimic what we know of the brain's visual cortex, which is still the best existing object detection system regarding speed and accuracy. For this paper we benchmarked two prominent implementations of these features, Mutch and Lowe's SLF-HMAX and Pinto et al.'s V1-like, against the popular local invariant features SIFT and PHOW in combination with the bag of visual words...
We present a novel probabilistic model for parsing shapes into several distinguishable parts for accurate shape recognition. This shape parsing is based on robust geometric features that permit high recognition accuracy. Although modelling shapes is an inherently uncertain process, our approach is lenient, in that the desired parse of a shape only needs to be within its k most probable parses. Using...
In order to realize model-based 3D object recognition, first, we propose a geometric feature extraction method based on a novel gaze modeling. In the modeling process, local surface models are independently estimated for parts of range data restricted by several gaze domains. Hence, since features are independently extracted from each gaze domain, inconsistent or incorrect features may be obtained...
In this paper we present a hybrid generative-discriminative approach for image categorization in real-world images, based on Latent Dirichlet Allocation and SVM classifiers. We use SVMs with non-linear kernels on different visual features in a multiple kernel combination framework. A major contribution of our work is also the introduction of a novel dataset, called MICC-Flickr101, based on the popular...
A model-based method for transformation-invariant area descriptor extraction is proposed in this paper in the context of object recognition and image matching. Local image descriptors are extracted in salient circular fragments of variable size, which indicate image locations with high intensity contrast, regional homogeneity and shape saliency. Three different types of descriptors — pose, intensity,...
We proposed in this paper a novel weighted longest increasing subsequence to improve the performance of the appearance-based object recognition. The LIS is employed to find the true keypoint matches that have consistent geometric order in both query and gallery images. Then, the similarity between query and gallery images is measured by the sum of the weights of the true keypoints. The experimental...
Recent studies have shown that K-means, with larger K, can effectively learn local image patch features; accompanied with appropriate pooling strategies, it performs very well in many visual object recognition tasks. An improved K-means cluster algorithm, GEV-Kmeans, based on the Generalized Extreme Value (GEV) distribution, is proposed in this paper. Our key observation is that the squared distance...
This paper presents a method for feature-based 3D object recognition in cluttered scenes. It deals with the problem of non-uniform sampling density which is inherent in typical range sensing methods. We suggest a method operating on polygonal meshes which overcomes the problem by exploiting surface area in both establishing local frames and creating feature descriptors. The method is able to recognize...
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