Image representations beyond histograms of gradients: The role of Gestalt descriptors

Bileschi, S.; Wolf, Lior

doi:10.1109/cvpr.2007.383122

Cited by 36 publications

(42 citation statements)

References 27 publications

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“…In this table we compare the results obtained using the proposed sketchable HoG descriptor with results of classical HoG descriptor and Berg in [1] and Bileschi in [2]. In order to do a comparison with reported results we used a Linear SVM.…”

Section: Resultsmentioning

confidence: 99%

“…In those classes with less samples than needed fewer images were used for test set. We use this protocol to be able to compare with results reported in [1] and [2].…”

Section: Resultsmentioning

confidence: 99%

“…The most similar approach to our method was proposed by Bileschi and Wolf in [2]. This method defines 4 different image features based on continuity, symmetry, closure and repetition.…”

Section: Discussion and Future Workmentioning

confidence: 99%

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Sketchable Histograms of Oriented Gradients for Object Detection

Zaytseva

Seguí

Vitrià

2012

Progress in Pattern Recognition, Image Analysis, Computer Vision, and Applications

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Abstract. In this paper we investigate a new representation approach for visual object recognition. The new representation, called sketchable-HoG, extends the classical histogram of oriented gradients (HoG) feature by adding two different aspects: the stability of the majority orientation and the continuity of gradient orientations. In this way, the sketchable-HoG locally characterizes the complexity of an object model and introduces global structure information while still keeping simplicity, compactness and robustness. We evaluated the proposed image descriptor on publicly Catltech 101 dataset. The obtained results outperforms classical HoG descriptor as well as other reported descriptors in the literature.

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Section: Resultsmentioning

confidence: 99%

“…In those classes with less samples than needed fewer images were used for test set. We use this protocol to be able to compare with results reported in [1] and [2].…”

Section: Resultsmentioning

confidence: 99%

See 1 more Smart Citation

Sketchable Histograms of Oriented Gradients for Object Detection

Zaytseva

Seguí

Vitrià

2012

Progress in Pattern Recognition, Image Analysis, Computer Vision, and Applications

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show abstract

“…Almost 60% of the data is covered by only 2 classes, and the rest is spread over the rest classes. And for the classes like car and door, Gestalt features (Bileschi and Wolf, 2007) may play major role in a good classification performance. We also believe symmetry and repetition features are vital for classifying window class.…”

Section: Discussionmentioning

confidence: 99%

“…In order to recover more precise boundaries, the work presented in this paper has been fused into conditional random field framework (Yang and Förstner, 2011) by including neighboring region information in the pairwise potential of the model, which allows us to reduce misclassification that occurs near the edges of objects. As future work, we are interested in evaluating more features, such as Gestalt features (Bileschi and Wolf, 2007) and other descriptor features (van de Sande et al, 2010), for building facade images.…”

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confidence: 99%

Feature Evaluation for Building Facade Images – An Empirical Study

Min-hua

Förstner

Chai

2012

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

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ABSTRACT:The classification of building facade images is a challenging problem that receives a great deal of attention in the photogrammetry community. Image classification is critically dependent on the features. In this paper, we perform an empirical feature evaluation task for building facade images. Feature sets we choose are basic features, color features, histogram features, Peucker features, texture features, and SIFT features. We present an approach for region-wise labeling using an efficient randomized decision forest classifier and local features. We conduct our experiments with building facade image classification on the eTRIMS dataset, where our focus is the object classes building, car, door, pavement, road, sky, vegetation, and window.

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PedVed: Pseudo Euclidian Distances for Video Events Detection

Sharif

Djeraba

2009

Advances in Visual Computing

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Image representations beyond histograms of gradients: The role of Gestalt descriptors

Cited by 36 publications

References 27 publications

Sketchable Histograms of Oriented Gradients for Object Detection

Sketchable Histograms of Oriented Gradients for Object Detection

Feature Evaluation for Building Facade Images – An Empirical Study

PedVed: Pseudo Euclidian Distances for Video Events Detection

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