The 2005 PASCAL Visual Object Classes Challenge

Everingham, Mark; Zisserman, Andrew; Williams, Christopher K. I.; Gool, Luc Van; Allan, Moray; Bishop, Christopher M.; Chapelle, Olivier; Dalal, Navneet; Deselaers, Thomas; Dorkó, Gyuri; Duffner, Stefan; Eichhorn, J; Farquhar, Jason; Fritz, Mario; García, Christophe; Griffiths, Tom; Jurie, Frédéric; Keysers, Daniel; Koskela, Markus; Laaksonen, Jorma; Larlus, Diane; Leibe, Bastian; Meng, Hongying; Ney, Hermann; Schiele, Bernt; Schmid, Cordelia; Seemann, Edgar; Shawe‐Taylor, John; Storkey, Amos; Szedmák, Sándor; Triggs, Bill; Ulusoy, İlkay; Viitaniemi, Ville; Zhang, Jianguo

doi:10.1007/11736790_8

Cited by 497 publications

(757 citation statements)

References 34 publications

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“…We carried out experiments on the VOC 2006 database [12] which contains images belonging to 10 categories. It is split into 1,277 train images and 2,686 test images.…”

Section: Results On Voc2006mentioning

confidence: 99%

Incremental kernel learning for active image retrieval without global dictionaries

Gosselin

Precioso

Philipp‐Foliguet

2011

Pattern Recognition

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In content-based image retrieval context, a classic strategy consists in computing off-line a dictionary of visual features. This visual dictionary is then used to provide a new representation of the data which should ease any task of classification or retrieval. This strategy, based on past research works in text retrieval, is suitable for the context of batch learning, when a large training set can be built either by using a strong prior knowledge of data semantics (like for textual data) or with an expensive off-line pre-computation. Such an approach has major drawbacks in the context of interactive retrieval, where the user iteratively builds the training data set in a semi-supervised approach by providing positive and negative annotations to the system in the relevance feedback loop. The training set is thus built for each retrieval session without any prior knowledge about the concepts of interest for this session. We propose a completely different approach to build the dictionary on-line from features extracted in relevant images. We design the corresponding kernel function, which is learnt during the retrieval session. For each new label, the kernel function is updated with a complexity linear with respect to the size of the database. We propose an efficient active learning strategy for the weakly supervised retrieval method developed in this paper. Moreover this framework allows the combination of features of different types. Experiments are carried out on standard databases, and show that a small dictionary can be dynamically extracted from the features with better performances than a global one.

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“…We carried out experiments on the VOC 2006 database [12] which contains images belonging to 10 categories. It is split into 1,277 train images and 2,686 test images.…”

Section: Results On Voc2006mentioning

confidence: 99%

Incremental kernel learning for active image retrieval without global dictionaries

Gosselin

Precioso

Philipp‐Foliguet

2011

Pattern Recognition

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“…Finally, we performed a recognition experiment on standard data from PASCAL VOC Challenge [4] to show the features performance in a different application scenario.…”

Section: Methodsmentioning

confidence: 99%

“…In this experiment, Pascal 2008 data [4] was used to compare segment based corner features to MSER/Hessian points all combined with SIFT. We applied pyramid match kernel (PMK) approach with SVM from [7] with 4 pyramid levels and branch factor equal 20.…”

Section: Object Recognitionmentioning

confidence: 99%

“…Techniques undertaken in [8,11,18] show that segmentation methods can be used in the recognition field. Since the state-of-the art recognition results [4] are still produced with interest point based methods, the advantages of segmentations for such purposes are yet to be proved with the appropriate approach. This paper reports on a set of tests which aimed at identifying what kinds of features from general-purpose segmentation algorithms come as stable.…”

Section: Introductionmentioning

confidence: 99%

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Segmentation Based Interest Points and Evaluation of Unsupervised Image Segmentation Methods

Koniusz¹,

Mikolajczyk²

2009

Procedings of the British Machine Vision Conference 2009

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This paper investigates segmentation based interest points for matching and recognition. We propose two simple methods for extracting features from the segmentation maps, which focus on the boundaries and centres of the gravity of the segments. In addition, this can be considered a novel approach for evaluating unsupervised image segmentation algorithms. Former evaluations aim at estimating segmentation quality by how well resulting segments adhere to the contours separating ground-truth foregrounds from backgrounds and therefore explicitly focus on particular objects of interest. In contrast, we propose to measure the robustness of segmentations by the repeatability of features extracted from segments on images related by various geometric and photometric transformations. Further, our evaluation provides a new insight into suitability of the segmentation methods for generating local features for image retrieval or recognition. Several segmentation methods are evaluated and compared to state-of-the art interest point detectors using the repeatability criteria as well as standard matching and recognition benchmarks.

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“…The use of color for object recognition has been extensively studied (van de Weijer and Schmid, 2006;van de Sande et al, 2010;Bosch et al, 2008;Everingham et al, 2009;Khan et al, 2012bKhan et al, , 2011. A variety of color descriptors and approaches to combining color and shape cues for object recognition have been proposed in the literature Schmid, 2007, 2006;van de Sande et al, 2010;Bosch et al, 2006;Vigo et al, 2010).…”

Section: Related Workmentioning

confidence: 99%

Coloring Action Recognition in Still Images

et al. 2013

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In this article we investigate the problem of human action recognition in static images. By action recognition we intend a class of problems which includes both action classification and action detection (i.e. simultaneous localization and classification). Bagof-words image representations yield promising results for action classification, and deformable part models perform very well object detection. The representations for action recognition typically use only shape cues and ignore color information. Inspired by the recent success of color in image classification and object detection, we investigate the potential of color for action classification and detection in static images.We perform a comprehensive evaluation of color descriptors and fusion approaches for action recognition. Experiments were conducted on the three datasets most used for benchmarking action recognition in still images: Willow, PASCAL VOC 2010 and Stanford-40. Our experiments demonstrate that incorporating color information considerably improves recognition performance, and that a descriptor based on color names outperforms pure color descriptors. Our experiments demonstrate that late fusion of color and shape information outperforms other approaches on action recognition. Finally, we show that the different color-shape fusion approaches result in complementary information and combining them yields state-of-the-art performance for action classification.

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The 2005 PASCAL Visual Object Classes Challenge

Cited by 497 publications

References 34 publications

Incremental kernel learning for active image retrieval without global dictionaries

Incremental kernel learning for active image retrieval without global dictionaries

Segmentation Based Interest Points and Evaluation of Unsupervised Image Segmentation Methods

Coloring Action Recognition in Still Images

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