Perceptually Motivated Shape Context Which Uses Shape Interiors

In this paper, we identify some of the limitations of current-day shape matching techniques. We provide examples of how contour-based shape matching techniques cannot provide a good match for certain visually similar shapes. To overcome this limitation, we propose a perceptually motivated variant of the well-known shape context descriptor. We identify that the interior properties of the shape play an important role in object recognition and develop a descriptor that captures these interior properties. We show that our method can easily be augmented with any other shape matching algorithm. We also show from our experiments that the use of our descriptor can significantly improve the retrieval rates

Improving Shape Retrieval by Integrating AIR and Modified Mutual k

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 mutual kNN 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 mutual kNN 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

Multiscale analysis of geometric planar deformations: application to wild animals electronic tracking and satellite ocean observation data

International audienceThe development of animal tracking technologies (including for instance GPS and ARGOS satellite systems) and the increasing resolution of remote sensing observations call for tools extracting and describing the geometric patterns along a track or within an image over a wide range of spatial scales. Whereas shape analysis has largely been addressed over the last decades, the multiscale analysis of the geometry of opened planar curves has received little attention. We here show that classical multiscale techniques cannot properly address this issue and propose an original wavelet-based scheme. To highlight the generic nature of our multiscale wavelet technique, we report applications to two different observation datasets, namely wild animal movement paths recorded by electronic tags and satellite observations of sea surface geophysical fields

ProDis-ContSHC: learning protein dissimilarity measures and hierarchical context coherently for protein-protein comparison in protein database retrieval

<p>Abstract</p> <p>Background</p> <p>The need to retrieve or classify protein molecules using structure or sequence-based similarity measures underlies a wide range of biomedical applications. Traditional protein search methods rely on a pairwise dissimilarity/similarity measure for comparing a pair of proteins. This kind of pairwise measures suffer from the limitation of neglecting the distribution of other proteins and thus cannot satisfy the need for high accuracy of the retrieval systems. Recent work in the machine learning community has shown that exploiting the global structure of the database and learning the contextual dissimilarity/similarity measures can improve the retrieval performance significantly. However, most existing contextual dissimilarity/similarity learning algorithms work in an unsupervised manner, which does not utilize the information of the known class labels of proteins in the database.</p> <p>Results</p> <p>In this paper, we propose a novel protein-protein dissimilarity learning algorithm, ProDis-ContSHC. ProDis-ContSHC regularizes an existing dissimilarity measure <it>d_ij</it>by considering the contextual information of the proteins. The context of a protein is defined by its neighboring proteins. The basic idea is, for a pair of proteins (<it>i</it>, <it>j</it>), if their context <inline-formula><m:math xmlns:m="http://www.w3.org/1998/Math/MathML" name="1471-2105-13-S7-S2-i1"><m:mi mathvariant="script">N</m:mi><m:mrow><m:mo class="MathClass-open">(</m:mo><m:mrow><m:mi>i</m:mi></m:mrow><m:mo class="MathClass-close">)</m:mo></m:mrow></m:math></inline-formula> and <inline-formula><m:math xmlns:m="http://www.w3.org/1998/Math/MathML" name="1471-2105-13-S7-S2-i2"><m:mi mathvariant="script">N</m:mi><m:mrow><m:mo class="MathClass-open">(</m:mo><m:mrow><m:mi>j</m:mi></m:mrow><m:mo class="MathClass-close">)</m:mo></m:mrow></m:math></inline-formula> is similar to each other, the two proteins should also have a high similarity. We implement this idea by regularizing <it>d_ij</it>by a factor learned from the context <inline-formula><m:math xmlns:m="http://www.w3.org/1998/Math/MathML" name="1471-2105-13-S7-S2-i3"><m:mi mathvariant="script">N</m:mi><m:mrow><m:mo class="MathClass-open">(</m:mo><m:mrow><m:mi>i</m:mi></m:mrow><m:mo class="MathClass-close">)</m:mo></m:mrow></m:math></inline-formula> and <inline-formula><m:math xmlns:m="http://www.w3.org/1998/Math/MathML" name="1471-2105-13-S7-S2-i4"><m:mi mathvariant="script">N</m:mi><m:mrow><m:mo class="MathClass-open">(</m:mo><m:mrow><m:mi>j</m:mi></m:mrow><m:mo class="MathClass-close">)</m:mo></m:mrow></m:math></inline-formula>.</p> <p>Moreover, we divide the context to hierarchial sub-context and get the contextual dissimilarity vector for each protein pair. Using the class label information of the proteins, we select the relevant (a pair of proteins that has the same class labels) and irrelevant (with different labels) protein pairs, and train an SVM model to distinguish between their contextual dissimilarity vectors. The SVM model is further used to learn a supervised regularizing factor. Finally, with the new <b>S</b>upervised learned <b>Dis</b>similarity measure, we update the <b>Pro</b>tein <b>H</b>ierarchial <b>Cont</b>ext <b>C</b>oherently in an iterative algorithm--<b>ProDis-ContSHC</b>.</p> <p>We test the performance of ProDis-ContSHC on two benchmark sets, i.e., the ASTRAL 1.73 database and the FSSP/DALI database. Experimental results demonstrate that plugging our supervised contextual dissimilarity measures into the retrieval systems significantly outperforms the context-free dissimilarity/similarity measures and other unsupervised contextual dissimilarity measures that do not use the class label information.</p> <p>Conclusions</p> <p>Using the contextual proteins with their class labels in the database, we can improve the accuracy of the pairwise dissimilarity/similarity measures dramatically for the protein retrieval tasks. In this work, for the first time, we propose the idea of supervised contextual dissimilarity learning, resulting in the ProDis-ContSHC algorithm. Among different contextual dissimilarity learning approaches that can be used to compare a pair of proteins, ProDis-ContSHC provides the highest accuracy. Finally, ProDis-ContSHC compares favorably with other methods reported in the recent literature.</p

Shape similarity analysis by self-tuning locally constrained mixed-diffusion

Similarity analysis is a powerful tool for shape matching/retrieval and other computer vision tasks. In the literature, various shape (dis)similarity measures have been introduced. Different measures specialize on different aspects of the data. In this paper, we consider the problem of improving retrieval accuracy by systematically fusing several different measures. To this end, we propose the locally constrained mixeddiffusion method, which partly fuses the given measures into one and propagates on the resulted locally dense data space. Furthermore, we advocate the use of self-adaptive neighborhoods to automatically determine the appropriate size of the neighborhoods in the diffusion process, with which the retrieval performance is comparable to the best manually tuned kNNs. The superiority of our approach is empirically demonstrated on both shape and image datasets. Our approach achieves a score of 100% in the bull’s eye test on the MPEG-7 shape dataset, which is the best reported result to date.Lei Luo, Chunhua Shen, Chunyuan Zhang and Anton van den Henge

Efficient Methods for Continuous and Discrete Shape Analysis

When interpreting an image of a given object, humans are able to abstract from the presented color information in order to really see the presented object. This abstraction is also known as shape. The concept of shape is not defined exactly in Computer Vision and in this work, we use three different forms of these definitions in order to acquire and analyze shapes. This work is devoted to improve the efficiency of methods that solve important applications of shape analysis. The most important problem in order to analyze shapes is the problem of shape acquisition. To simplify this very challenging problem, numerous researchers have incorporated prior knowledge into the acquisition of shapes. We will present the first approach to acquire shapes given a certain shape knowledge that computes always the global minimum of the involved functional which incorporates a Mumford-Shah like functional with a certain class of shape priors including statistic shape prior and dynamical shape prior. In order to analyze shapes, it is not only important to acquire shapes, but also to classify shapes. In this work, we follow the concept of defining a distance function that measures the dissimilarity of two given shapes. There are two different ways of obtaining such a distance function that we address in this work. Firstly, we model the set of all shapes as a metric space induced by the shortest path on an orbifold. The shortest path will provide us with a shape morphing, i.e., a continuous transformation from one shape into another. Secondly, we address the problem of shape matching that finds corresponding points on two shapes with respect to a preselected feature. Our main contribution for the problem of shape morphing lies in the immense acceleration of the morphing computation. Instead of solving partial resp. ordinary differential equations, we are able to solve this problem via a gradient descent approach that subsequently shortens the length of a path on the given manifold. During our runtime test, we observed a run-time acceleration of up to a factor of 1000. Shape matching is a classical discrete problem. If each shape is discretized by N shape points, most Computer Vision methods needed a cubic run-time. We will provide two approaches how to reduce this worst-case complexity to O(N2 log(N)). One approach exploits the planarity of the involved graph in order to efficiently compute N shortest path in a graph of O(N2) vertices. The other approach computes a minimal cut in a planar graph in O(N log(N)). In order to make this approach applicable to shape matching, we improved the run-time of a recently developed graph cut approach by an empirical factor of 2–4

Unsupervised Measures For Estimating The Effectiveness Of Image Retrieval Systems

The main objective of Content-Based Image Retrieval (CBIR) systems is to retrieve a ranked list containing the most similar images of a collection given a query image, by taking into account their visual content. Although these systems represent a very promising approach, in many situations is very challenging to assure the quality of returned ranked lists. Supervised approaches rely on training data and information obtained from user interactions to identify and then improve low-quality results. However, these approaches require a lot of human efforts which can be infeasible for many systems. In this paper, we present two novel unsupervised measures for estimating the effectiveness of ranked lists in CBIR tasks. Given an estimation of the effectiveness of ranked lists, many CBIR systems can, for example, emulate the training process, but now without any user intervention. Improvements can also be achieved on several unsupervised approaches, such as re-ranking and rank aggregation methods, once the estimation measures can help to consider more relevant information by distinguishing effective from non-effective ranked lists. Both proposed measures are computed using a novel image representation of ranked lists and distances among images considering a given dataset. The objective is to exploit the visual patterns encoded in the image representations for estimating the effectiveness of ranked lists. Experiments involving shape, color, and texture descriptors demonstrate that the proposed approaches can provide accurate estimations of the quality in terms of effectiveness of ranked lists. The use of proposed measures are also evaluated in image retrieval tasks aiming at improving the effectiveness of rank aggregation approaches. © 2013 IEEE.341348Datta, R., Joshi, D., Li, J., Wang, J.Z., Image retrieval: Ideas, influences, and trends of the new age (2008) ACM Computing Surveys, 40 (2), pp. 51-560Da S Torres, R., Falcao, A.X., Content-based image retrieval: Theory and applications (2006) Revista de Inforḿatica Téorica e Aplicada, 13 (2), pp. 161-185Ferreira, C.D., Dos Santos, J.A., Da S Torres, R., Gonçalves, M.A., Rezende, R.C., Fan, W., Relevance feedback based on genetic programming for image retrieval (2011) Pattern Recogninion Letters, 32 (1), pp. 27-37Dos Santos, J.A., Ferreira, C.D., Da S Torres, R., Gonçalves, M.A., Lamparelli, R.A., A relevance feedback method based on genetic programming for classification of remote sensing images (2011) Information Sciences, 181 (13), pp. 2671-2684Kontschieder, P., Donoser, M., Bischof, H., Beyond pairwise shape similarity analysis (2009) Asian Conference on Computer Vision, pp. 655-666Yang, X., Bai, X., Latecki, L.J., Tu, Z., Improving shape retrieval by learning graph transduction (2008) European Conference on Computer Vision (ECCV'2008), 4, pp. 788-801Jiang, J., Wang, B., Tu, Z., Unsupervised metric learning by selfsmoothing operator (2011) IEEE International Conference on Computer Vision (ICCV'2011), pp. 794-801Yang, X., Prasad, L., Latecki, L., Affinity learning with diffusion on tensor product graph (2013) Pattern Analysis and Machine Intelligence, IEEE Transactions on, 35 (1), pp. 28-38Yang, X., Latecki, L.J., Affinity learning on a tensor product graph with applications to shape and image retrieval (2011) IEEE Conference on Computer Vision and Pattern Recognition (CVPR'2011), pp. 2369-2376Pedronette, D.C.G., Da S Torres, R., Exploiting pairwise recommendation and clustering strategies for image re-ranking (2012) Information Sciences, 207 (1), pp. 19-34Shen, X., Lin, Z., Brandt, J., Avidan, S., Wu, Y., Object retrieval and localization with spatially-constrained similarity measure and knn re-ranking (2012) IEEE Conference on Computer Vision and Pattern Recognition (CVPR'2012), pp. 3013-3020Qin, D., Gammeter, S., Bossard, L., Quack, T., Van Gool, L., Hello neighbor: Accurate object retrieval with k-reciprocal nearest neighbors (2011) IEEE Conference on Computer Vision and Pattern Recognition (CVPR'2011), pp. 777-784. , juneSchwander, O., Nielsen, F., Reranking with contextual dissimilarity measures from representational bregmanl k-means (2010) International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISAPP'2010), 1, pp. 118-122Pedronette, D.C.G., Da S Torres, R., Image re-ranking and rank aggregation based on similarity of ranked lists (2011) Computer Analysis of Images and Patterns (CAIP'2011), 6854, pp. 369-376Pedronette, D.C.G., Da S Torres, R., Exploiting contextual information for rank aggregation (2011) International Conference on Image Processing (ICIP'2011), pp. 97-100Pedronette, D.C.G., Da S Torres, R., Exploiting contextual information for image re-ranking and rank aggregation (2012) International Journal of Multimedia Information Retrieval, 1 (2), pp. 115-128Latecki, L.J., Lakmper, R., Eckhardt, U., Shape descriptors for non-rigid shapes with a single closed contour (2000) IEEE Conference on Computer Vision and Pattern Recognition (CVPR'2000), pp. 424-429Pedronette, D.C.G., Da S Torres, R., Shape retrieval using contour features and distance optmization (2010) International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISAPP'2010), 1, pp. 197-202Da S Torres, R., Falcao, A.X., Contour salience descriptors for effective image retrieval and analysis (2007) Image and Vision Computing, 25 (1), pp. 3-13Arica, N., Vural, F.T.Y., BAS: A perceptual shape descriptor based on the beam angle statistics (2003) Pattern Recognition Letters, 24 (9-10), pp. 1627-1639Ling, H., Jacobs, D.W., Shape classification using the innerdistance (2007) IEEE Transactions on Pattern Analysis and Machine Intelligence, 29 (2), pp. 286-299Ling, H., Yang, X., Latecki, L.J., Balancing deformability and discriminability for shape matching (2010) European Conference on Computer Vision (ECCV'2010), 3, pp. 411-424Stehling, R.O., Nascimento, M.A., Falcao, A.X., A compact and efficient image retrieval approach based on border/interior pixel classification (2002) ACM Conference on Information and Knowledge Management (CIKM'2002), pp. 102-109Huang, J., Kumar, S.R., Mitra, M., Zhu, W.-J., Zabih, R., Image indexing using color correlograms (1997) IEEE Conference on Computer Vision and Pattern Recognition (CVPR'97), pp. 762-768Swain, M.J., Ballard, D.H., Color indexing (1991) International Journal on Computer Vision, 7 (1), pp. 11-32Weijer De J.Van, Schmid, C., Coloring local feature extraction (2006) European Conference on Computer Vision (ECCV'2006), (PART 2), pp. 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Image Re-ranking Acceleration On Gpus

Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)Huge image collections are becoming available lately. In this scenario, the use of Content-Based Image Retrieval (CBIR) systems has emerged as a promising approach to support image searches. The objective of CBIR systems is to retrieve the most similar images in a collection, given a query image, by taking into account image visual properties such as texture, color, and shape. In these systems, the effectiveness of the retrieval process depends heavily on the accuracy of ranking approaches. Recently, re-ranking approaches have been proposed to improve the effectiveness of CBIR systems by taking into account the relationships among images. The re-ranking approaches consider the relationships among all images in a given dataset These approaches typically demands a huge amount of computational power, which hampers its use in practical situations. On the other hand, these methods can be massively parallelized. In this paper, we propose to speedup the computation of the RL-Sim algorithm, a recently proposed image re-ranking approach, by using the computational power of Graphics Processing Units (GPU). GPUs are emerging as relatively inexpensive parallel processors that are becoming available on a wide range of computer systems. We address the image re-ranking performance challenges by proposing a parallel solution designed to fit the computational model of GPUs. We conducted an experimental evaluation considering different implementations and devices. Experimental results demonstrate that significant performance gains can be obtained. Our approach achieves speedups of 7 × from serial implementation considering the overall algorithm and up to 36 × on its core steps. © 2013 IEEE.176183Brazilian Computer Society (SBC),Brazilian Funding Agencies CAPES,CNPq,et al.,IEEE Computer Society Through the Technical Committees,on Computer Architecture (TCCA) and TCSCConselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)Datta, R., Joshi, D., Li, J., Wang, J.Z., Image retrieval: Ideas, influences, and trends of the new age (2008) ACM Computing Surveys, 40 (2), pp. 51-560McDonald, S., Tait, J., Search strategies in content-based image retrieval (2003) 26th ACM SIGIR Conference on Research and Development in Informaion Retrieval (SIGIR'03), pp. 80-87Ferreira, C.D., Dos Santos, J.A., Da Torres, S.R., Gonçalves, M.A., Rezende, R.C., Fan, W., Relevance feedback based on genetic programming for image retrieval (2011) Pattern Recogninion Letters, 32 (1), pp. 27-37Dos Santos, J.A., Ferreira, C.D., Da Torres, S.R., Gonçalves, M.A., Lamparelli, R.A., A relevance feedback method based on genetic programming for classification of remote sensing images (2011) Information Sciences, 181 (13), pp. 2671-2684Pedronette, D.C.G., Da Torres, S.R., Image re-ranking and rank aggregation based on similarity of ranked lists (2013) Pattern Recognition, , to appear http://dx.doi.org/10.1016/j.patcog.2013.01.004Yang, X., Prasad, L., Latecki, L., Affinity learning with diffusion on tensor product graph (2012) Pattern Analysis and Machine Intelligence, PP (99), p. 1. , IEEE Transactions onYang, X., Latecki, L.J., Affinity learning on a tensor product graph with applications to shape and image retrieval (2011) IEEE Conference on Computer Vision and Pattern Recognition (CVPR'2011), pp. 2369-2376Pedronette, D.C.G., Da Torres, S.R., Exploiting pairwise recommendation and clustering strategies for image re-ranking (2012) Information Sciences, 207, pp. 19-34Jegou, H., Schmid, C., Harzallah, H., Verbeek, J., Accurate image search using the contextual dissimilarity measure (2010) IEEE Transactions on Pattern Analysis and Machine Intelligence, 32 (1), pp. 2-11Pedronette, D.C.G., Da Torres, S.R., Borin, E., Breternitz, M., Efficient image re-ranking computation on GPUs (2012) Int. 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Abgründe der Informatik : Geheimnisse und Gemeinheiten

Content-based Image Retrieval (CBIR) systems consider only a pairwise analysis, i.e., they measure the similarity between pairs of images, ignoring the rich information encoded in the relations among several images. However, the user perception usually considers the query specification and responses in a given context. In this scenario, re-ranking methods have been proposed to exploit the contextual information and, hence, improve the effectiveness of CBIR systems. Besides the effectiveness, the usefulness of those systems in real-world applications also depends on the efficiency and scalability of the retrieval process, imposing a great challenge to the re-ranking approaches, once they usually require the computation of distances among all the images of a given collection. In this paper, we present a novel approach for the re-ranking problem. It relies on the similarity of top-k lists produced by efficient indexing structures, instead of using distance information from the entire collection. Extensive experiments were conducted on a large image collection, using several indexing structures. Results from a rigorous experimental protocol show that the proposed method can obtain significant effectiveness gains (up to 12.19% better) and, at the same time, improve considerably the efficiency (up to 73.11% faster). In addition, our technique scales up very well, which makes it suitable for large collections. © 2013 Elsevier Inc. All rights reserved.26591104Almeida, J., Leite, N.J., Torres, R.S., Comparison of video sequences with histograms of motion patterns (2011) IEEE Int. Conf. Image Processing (ICIP'11), pp. 3673-3676Almeida, J., Rocha, A., Torres, R.S., Goldenstein, S., Making colors worth more than a thousand words (2008) ACM Int. Symp. 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