Multilevel Image Coding with Hyperfeatures

Agarwal, Ankur; Triggs, Bill

doi:10.1007/s11263-007-0072-x

Cited by 39 publications

(40 citation statements)

References 38 publications

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“…An evaluation and comparison of several spatial recognition methods has been presented by Mikolajczyk and Schmid [47]. Dense local approaches have in turn been investigated by Jurie and Triggs [27], Lazebnik et al [34], Bosch et al [5] and Agarwal and Triggs [1].…”

Section: Related Workmentioning

confidence: 99%

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Composed complex-cue histograms: An investigation of the information content in receptive field based image descriptors for object recognition

Linde

Lindeberg

2012

Computer Vision and Image Understanding

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Recent work has shown that effective methods for recognizing objects and spatio-temporal events can be constructed based on histograms of receptive field like image operations.This paper presents the results of an extensive study of the performance of different types of receptive field like image descriptors for histogram-based object recognition, based on different combinations of image cues in terms of Gaussian derivatives or differential invariants applied to either intensity information, colour-opponent channels or both. A rich set of composed complex-cue image descriptors is introduced and evaluated with respect to the problems of (i) recognizing previously seen object instances from previously unseen views, and (ii) classifying previously unseen objects into visual categories.It is shown that there exist novel histogram descriptors with significantly better recognition performance compared to previously used histogram features within the same class. Specifically, the experiments show that it is possible to obtain more discriminative features by combining lower-dimensional scale-space features into composed complexcue histograms. Furthermore, different types of image descriptors have different relative advantages with respect to the problems of object instance recognition vs. object category classification. These conclusions are obtained from extensive evaluations on two mutually independent data sets.For the task of recognizing specific object instances, combined histograms of spatial and spatio-chromatic derivatives are highly discriminative, and several image descriptors in terms rotationally invariant (intensity and spatiochromatic) differential invariants up to order two lead to very high recognition rates.For category classification, primary information is contained in both first-and second-order derivatives, where second-order partial derivatives constitute the most discriminative cue.Dimensionality reduction by principal component analysis and variance normalization prior to training and recognition can in many cases lead to a significant increase in recognition or classification performance. Surprisingly high recognition rates can even be obtained with binary histograms that reveal the polarity of local scale-space features, and which can be expected to be particularly robust to illumination variations. An overall conclusion from this study is that compared to previously used lower-dimensional histograms, the use of composed complex-cue histograms of higher dimensionality reveals the co-variation of multiple cues and enables much better recognition performance, both with regard to the problems of recognizing previously seen objects from novel views and for classifying previously unseen objects into visual categories.

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Section: Related Workmentioning

confidence: 99%

“…invariants For colour images, we also define chromatic cues (c (1) , c (2) ) from RGB images by red/green and yellow/blue colour-opponent channels according to [25] …”

Section: Spatio-chromatic Derivatives and Differentialmentioning

confidence: 99%

Composed complex-cue histograms: An investigation of the information content in receptive field based image descriptors for object recognition

Linde

Lindeberg

2012

Computer Vision and Image Understanding

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“…where g : R 2 ×R + → R denotes the (rotationally symmetric) Gaussian kernel g(x, y; t) = 1 2π t e −(x 2 +y 2 )/2t (2) and the variance t = σ 2 of this kernel is referred to as the scale parameter. Equivalently, the scale-space family can be obtained as the solution of the (linear) diffusion equation…”

Section: Scale-space Representationmentioning

confidence: 99%

Image Matching Using Generalized Scale-Space Interest Points

Lindeberg

2013

Lecture Notes in Computer Science

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The performance of matching and object recognition methods based on interest points depends on both the properties of the underlying interest points and the choice of associated image descriptors. This paper demonstrates advantages of using generalized scale-space interest point detectors in this context for selecting a sparse set of points for computing image descriptors for image-based matching. For detecting interest points at any given scale, we make use of the Laplacian ∇ 2 norm L, the determinant of the Hessian det H norm L and four new unsigned or signed Hessian feature strength measures D 1,norm L,D 1,norm L, D 2,norm L and D 2,norm L, which are defined by generalizing the definitions of the Harris and Shi-and-Tomasi operators from the second moment matrix to the Hessian matrix. Then, feature selection over different scales is performed either by scale selection from local extrema over scale of scale-normalized derivates or by linking features over scale into feature trajectories and computing a significance measure from an integrated measure of normalized feature strength over scale. A theoretical analysis is presented of the robustness of the differential entities underlying these interest points under image deformations, in terms of invariance properties under affine image deformations or approximations thereof. Disregarding the effect of the rotationally symmetric scale-space smoothing operation, the determinant of the Hessian det H norm L is a truly affine covariant differential entity and the Hessian feature strength measures D 1,norm L andD 1,norm L have a major contribution from the affine covariant determinant of the Hessian, implying that local extrema of these differen- It is shown how these generalized scale-space interest points allow for a higher ratio of correct matches and a lower ratio of false matches compared to previously known interest point detectors within the same class. The best results are obtained using interest points computed with scale linking and with the new Hessian feature strength measures D 1,norm L,D 1,norm L and the determinant of the Hessian det H norm L being the differential entities that lead to the best matching performance under perspective image transformations with significant foreshortening, and better than the more commonly used Laplacian operator, its difference-of-Gaussians approximation or the Harris-Laplace operator. We propose that these generalized scale-space interest points, when accompanied by associated local scale-invariant image descriptors, should allow for better performance of interest point based methods for image-based matching, object recognition and related visual tasks.

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“…Bringing context of neighboring features to define "higher level" features is clearly recognized as the next natural step here. As described in section 1, hyperfeatures [14] were devised especially to fulfil this need. In this paper, we continue to explore this middle ground.…”

Section: Beyond Bag Of Wordsmentioning

confidence: 99%

“…Hyperfeatures [14] exploit the spatial co-occurrence statistics at scales larger than their local input patches by aggregating local descriptors using methods such as GMM and LDA. This paper is a step forward in this direction and tries to combine the strengths of hierarchical feature learning and BoW learning paradigms.…”

Section: Introductionmentioning

confidence: 99%

Learning Hierarchical Bag of Words Using Naive Bayes Clustering

Chandra

Kumar²,

Jawahar

2013

Computer Vision – ACCV 2012

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Abstract. Image analysis tasks such as classification, clustering, detection, and retrieval are only as good as the feature representation of the images they use. Much research in computer vision is focused on finding better or semantically richer image representations. Bag of visual Words (BoW) is a representation that has emerged as an effective one for a variety of computer vision tasks. BoW methods traditionally use low level features. We have devised a strategy to use these low level features to create "higher level" features by making use of the spatial context in images. In this paper, we propose a novel hierarchical feature learning framework that uses a Naive Bayes Clustering algorithm to convert a 2-D symbolic image at one level to a 2-D symbolic image at the next level with richer features. On two popular datasets, Pascal VOC 2007 and Caltech 101, we empirically show that classification accuracy obtained from the hierarchical features computed using our approach is significantly higher than the traditional SIFT based BoW representation of images even though our image representations are more compact.

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Multilevel Image Coding with Hyperfeatures

Cited by 39 publications

References 38 publications

Composed complex-cue histograms: An investigation of the information content in receptive field based image descriptors for object recognition

Composed complex-cue histograms: An investigation of the information content in receptive field based image descriptors for object recognition

Image Matching Using Generalized Scale-Space Interest Points

Learning Hierarchical Bag of Words Using Naive Bayes Clustering

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