Feature context for image classification and object detection

Wang, Xinggang; Bai, Xiang; Liu, Wenyu; Latecki, Longin Jan

doi:10.1109/cvpr.2011.5995696

Cited by 69 publications

(19 citation statements)

References 25 publications

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“…While we can obtain good representations of original features using the codebook, there still information loss during the quantization due to product constraint. In order to further improve the representation, inspire by [24], we propose a simple but effective information supplementation method by the adding residual information.…”

Section: Encoding Residualsmentioning

confidence: 99%

Sketch4Image: a novel framework for sketch-based image retrieval based on product quantization with coding residuals

Han

Dang

2015

Multimed Tools Appl

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Sketch-based Image Retrieval (SBIR) is one important branch of Content-based Image Retrieval (CBIR). SBIR means dealing with retrieval using simple edge or contour images. However, SBIR is more difficult than CBIR due to the lack of visual information, this makes the Bag-of-Words (BoW) or codebook in SBIR hard to construct. In this paper, we propose a novel SBIR framework based on Product Quantization (PQ) with sparse coding (SC) to construct an optimized codebook. By using state-of-the-art local descriptors, we transform sketch images into features and then build the optimized codebook using PQbased SC. In the retrieval stage, we can obtain a better representation of the query sketch and testing images by the optimized codebook with coding quantization residuals, by which the information loss during feature encoding process can be reduced; similarity computing is implemented by comparing the feature histograms between a query sketch and the testing data for the final results. We demonstrate the superiority and effectiveness of the proposed SBIR by comparing it with several state-of-the-art methods on three public sketch datasets.

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Section: Encoding Residualsmentioning

confidence: 99%

Sketch4Image: a novel framework for sketch-based image retrieval based on product quantization with coding residuals

Han

Dang

2015

Multimed Tools Appl

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“…Contour flexibility feature extraction method was developed for feature extraction of both local and global features [27]. Bag of contour fragments (BCF) is an extended form of Bag-of-Words (BoW) for shape images that were used in [28]. In BCF, shape is divided into set of contour fragments and each contour is calculated by SC which follow the encoded into shape code.…”

Section: Related Workmentioning

confidence: 99%

Improving Shape Retrieval by Integrating AIR and Modified MutualkNN Graph

Qadeer

Liu

et al. 2015

Advances in Multimedia

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In computer vision, image retrieval remained a significant problem and recent resurgent of image retrieval also relies on other postprocessing methods to improve the accuracy instead of solely relying on good feature representation. Our method addressed the shape retrieval of binary images. This paper proposes a new integration scheme to best utilize feature representation along with contextual information. For feature representation we used articulation invariant representation; dynamic programming is then utilized for better shape matching followed by manifold learning based postprocessing modified mutualkNN graph to further improve the similarity score. We conducted extensive experiments on widely used MPEG-7 database of shape images by so-called bulls-eye score with and without normalization of modified mutualkNN graph which clearly indicates the importance of normalization. Finally, our method demonstrated better results compared to other methods. We also computed the computational time with another graph transduction method which clearly shows that our method is computationally very fast. Furthermore, to show consistency of postprocessing method, we also performed experiments on challenging ORL and YALE face datasets and improved baseline results.

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“…The novel spatial pyramid layout used by the winner of VOC 2007 [21] has been adopted by many recent state-of-the-art methods [22][23][24]. In [25], fan-shaped areas are used in place of rectangular spatial areas in SPR. In contrast to these aforementioned methods, our work effectively and efficiently processes a complete set of rectangular windows, instead of a handcrafted subset.…”

Section: Related Workmentioning

confidence: 99%

Beyond Spatial Pyramids: A New Feature Extraction Framework with Dense Spatial Sampling for Image Classification

Yan

et al. 2012

Computer Vision – ECCV 2012

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Abstract. We introduce a new framework for image classification that extends beyond the window sampling of fixed spatial pyramids to include a comprehensive set of windows densely sampled over location, size and aspect ratio. To effectively deal with this large set of windows, we derive a concise high-level image feature using a two-level extraction method. At the first level, window-based features are computed from local descriptors (e.g., SIFT, spatial HOG, LBP) in a process similar to standard feature extractors. Then at the second level, the new image feature is determined from the window-based features in a manner analogous to the first level. This higher level of abstraction offers both efficient handling of dense samples and reduced sensitivity to misalignment. More importantly, our simple yet effective framework can readily accommodate a large number of existing pooling/coding methods, allowing them to extract features beyond the spatial pyramid representation. To effectively fuse the second level feature with a standard first level image feature for classification, we additionally propose a new learning algorithm, called Generalized Adaptive p-norm Multiple Kernel Learning (GA-MKL), to learn an adapted robust classifier based on multiple base kernels constructed from image features and multiple sets of prelearned classifiers of all the classes. Extensive evaluation on the object recognition (Caltech256) and scene recognition (15Scenes) benchmark datasets demonstrates that the proposed method outperforms state-ofthe-art image classification algorithms under a broad range of settings.

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Feature context for image classification and object detection

Cited by 69 publications

References 25 publications

Sketch4Image: a novel framework for sketch-based image retrieval based on product quantization with coding residuals

Sketch4Image: a novel framework for sketch-based image retrieval based on product quantization with coding residuals

Improving Shape Retrieval by Integrating AIR and Modified MutualkNN Graph

Beyond Spatial Pyramids: A New Feature Extraction Framework with Dense Spatial Sampling for Image Classification

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