Query Shape Research Articles

Presto's History-Based Query Optimizer

An important feature of modern query optimizers is the ability to produce a query plan that is optimal for the underlying data set. This requires the ability to estimate cardinalities and computational costs of intermediate query plan nodes, which is highly dependent on both the query shape and the underlying data distribution. Traditional methods include collecting statistics on base tables and implementing cardinality and computational cost derivation inside the optimizer, which is error-prone for complex query shapes. This paper presents Presto's novel history-based optimization framework (HBO), which collects execution histories and uses them to optimize similar queries in the future. The framework produces accurate estimates for complex query shapes in a lightweight, automated manner, and adapts automatically to changes in underlying data distributions. We present the design and implementation of the HBO framework and provide details on its use in various optimization rules, as well as details on implementing the statistics store on top of a Redis key-value store. We also present the results of running HBO in production in two large data infrastructure organizations (Meta and Uber).

Sampling and sparsification for approximating the packedness of trajectories and detecting gatherings

A polygonal curve $P$ with $n$ vertices is $c$-packed, if the sum of the lengths of the parts of the edges of the curve that are inside any disk of radius $r$ is at most $cr$, for any $r>0$. Similarly, the concept of $c$-packedness can be defined for any scaling of a given shape. Assuming $L$ is the diameter of $P$ and $\delta$ is the minimum distance between points on disjoint edges of $P$, we show the approximation factor of the existing $O(\frac{\log (L/\delta)}{\epsilon}n^3)$ time algorithm is $1+\epsilon$-approximation algorithm. The massively parallel versions of these algorithms run in $O(\log (L/\delta))$ rounds. We improve the existing $O((\frac{n}{\epsilon^3})^{\frac 4 3}\polylog \frac n \epsilon)$ time $(6+\epsilon)$-approximation algorithm by providing a $(4+\epsilon)$-approximation $O(n(\log^2 n)(\log^2 \frac{1}{\epsilon})+\frac{n}{\epsilon})$ time algorithm, and the existing $O(n^2)$ time $2$-approximation algorithm improving the existing $O(n^2\log n)$ time $2$-approximation algorithm. Our exact $c$-packedness algorithm takes $O(n^5)$ time, which is the first exact algorithm for disks. We show using $\alpha$-fat shapes instead of disks adds a factor $\alpha^2$ to the approximation. We also give a data-structure for computing the curve-length inside query disks. It has $O(n^6\log n)$ construction time, uses $O(n^6)$ space, and has query time $O(\log n+k)$, where $k$ is the number of intersected segments with the query shape. We also give a massively parallel algorithm for relative $c$-packedness with $O(1)$ rounds.

AgentDress: Realtime Clothing Synthesis for Virtual Agents using Plausible Deformations.

We present a CPU-based real-time cloth animation method for dressing virtual humans of various shapes and poses. Our approach formulates the clothing deformation as a high-dimensional function of body shape parameters and pose parameters. In order to accelerate the computation, our formulation factorizes the clothing deformation into two independent components: the deformation introduced by body pose variation (Clothing Pose Model) and the deformation from body shape variation (Clothing Shape Model). Furthermore, we sample and cluster the poses spanning the entire pose space and use those clusters to efficiently calculate the anchoring points. We also introduce a sensitivity-based distance measurement to both find nearby anchoring points and evaluate their contributions to the final animation. Given a query shape and pose of the virtual agent, we synthesize the resulting clothing deformation by blending the Taylor expansion results of nearby anchoring points. Compared to previous methods, our approach is general and able to add the shape dimension to any clothing pose model. Furthermore, we can animate clothing represented with tens of thousands of vertices at 50+ FPS on a CPU. We also conduct a user evaluation and show that our method can improve a user's perception of dressed virtual agents in an immersive virtual environment (IVE) compared to a realtime linear blend skinning method.

Bag of feature with discriminative module for non-rigid shape retrieval

Non-grid shape retrieval has been an important and challenging task that usually requires high-level and discriminative shape features. However, most of the existing descriptors are hand-crafted, which fail to distinguish and focus on important features from different regions and scales for given query shape. To tackle this issue, this paper presents a novel shape retrieval method, namely BoF-DM (Bag of feature with Discriminative Module), which is specially designed to highlight discriminative features from the multi-scale context structures. Specifically, a shape decomposing method is designed by combining the KPs (key points) and ring graph, result in local to global feature learning. To obtain high-level shape features, the spatial context of each contour fragment is learned by the stacked lightweight network in an unsupervised manner. In people's cognition, discriminative parts, such as heads or legs, play an essential role for identifying animal shape classes. Inspired by this phenomenon, we propose a TF-IDF (term frequency-inverse document frequency) with entropy-based Discriminative Module, which fully considers the attention issue of inter-class difference and intra-class similarity. Finally, decision-making is done by metric similarity. The experiments demonstrate that the proposed method achieves the high BER (Bull's Eye Retrieval) rates of 93.63%, 100%, 100%, and 99.00% on the considered MPEG-7, Kimia's 99, Kimia's 216, and Articulated shape datasets respectively. Furthermore, our method also obtains high performance under noisy and articulated conditions.

Windowing queries using Minkowski sum and their extension to MapReduce

Given a set of n segments and a query shape Q, the windowing length query asks for finding the sum of the lengths of the parts of the segments that lie inside Q. The popular places problem of a set of curves asks for the subset of the plane where each query shape centered at a point of that region intersects with at least f distinct curves. For square queries, an optimal $$O(n^2)$$ time algorithm and a matching lower bound exist. We solve the length query problem for convex polygons and disks as query shapes, with $$O(\log n+k)$$ query time and polynomial preprocessing time that depends on the complexity of the query shape. We define a new version of the problem of finding popular places in a set of trajectories where the center of a query is a popular place if the length of the curves inside that query is at least f and use our data structure to solve the original problem as well as this new version. Other than length queries, we solve reporting queries that return the set of intersected segments. For disk queries, we design a point-location data structure for congruent disks with $$O(\log n)$$ query time and $$O(n^3\log n)$$ preprocessing. We also give algorithms for computing the length query for c-packed curves, which are a class of curves for which the length of the curve inside a disk of radius r is upper-bounded by cr, where c is a constant. Also, we use length queries for polygons to approximate the minimum value c for which a curve is c-packed, if such a c exists. Our results extend to MRC and MPC models for MapReduce, where we address these problems on a set of x-monotone curves. The round complexities of our MapReduce algorithms are constant. In addition, we also implemented our popular places algorithms on trajectories on inputs as big as 15K points to evaluate the efficiency of our algorithms in practice.

A fully spatial personalized differentially private mechanism to provide non-uniform privacy guarantees for spatial databases

Spatial databases are essential to applications in a wide variety of domains. One of the main privacy concerns when answering statistical queries, such as range counting queries, over a spatial database is that an adversary observing changes in query answers may be able to determine whether or not a particular geometric object is present in the database. Differential privacy addresses this concern by guaranteeing that the presence or absence of a geometric object has little effect on query answers. Most of the current differentially private mechanisms for spatial databases ignore the fact that privacy is personal and, thus, provide the same privacy protection for all geometric objects. However, some particular geometric objects may be more sensitive to privacy issues than others, requiring stronger differential privacy guarantees. In this paper, we introduce the concept of spatial personalized differential privacy for spatial databases where different geometric objects have different privacy protection requirements. Also, we present SPDP-PCE, a novel spatial personalized differentially private mechanism to answer range counting queries over spatial databases that fully considers the privacy protection requirements of geometric objects in the underlying geometric space in both steps of noise addition and consistency enforcement. Our experimental results on real datasets demonstrate the effectiveness of SPDP-PCE under various total privacy budgets, query shapes, and privacy level distributions.

3D skeleton transfer for meshes and clouds

We present a curve-skeleton extraction method in the form of curve-skeleton transfer from the source shape to the target. The shapes we deal with need to be in correspondence. They are discretized as meshes embedded in 3D and are not necessarily watertight. They can even be in arbitrary topology, and furthermore be disconnected point clouds. Our method associates the source skeleton with the source shape and computes the optimal rigid transformations towards the corresponding query shape points. This transformation for each source skeleton point is followed by a post-processing operation. Fixed skeleton connectivity maintained throughout the transfer guarantees that the resulting skeleton is as complex as the source skeleton, e.g., no redundant branches and complicated joint hierarchies. The performance and versatility of our method as well as its advantages over a direct skeleton extraction method are demonstrated.

O2O Method for Fast 2D Shape Retrieval.

A novel post-processing method, online to offline (O2O), to improve the efficiency of shape retrieval is proposed in this paper. The essence of this proposed method is to move more work that requires a lot of computation to offline. Based on this approach, the O2O rerank the retrieval result online with the help of the offline analysis. The result of offline analysis can be reused indefinitely, regardless of the query shape, as long as the database is unchanged. Therefore, O2O is very efficient and suitable for real-time applications. We evaluated our method for shape retrieval and recognition on five databases including MPEG-7 CE-1 Part B, Tari 1000, Animals, Kimia 99, and Swedish Plant Leaf. Our experimental results show that as a post-processing algorithm, O2O provides highly efficient and effective shape retrieval.

Indonesian Sign Language Recognition Based on Shape of Hand Gesture

In this research the proposes a method of recognition of BISINDO letters based on hand-shape features that hint every shape of BISINDO Letters. In outline, this method is divided into two parts: the first is part of formation database shape features of BISINDO letters A-Z and the second is part of BISINDO letters recognition. In the first section consist of hand-shape image acquisition that hint every BISINDO letters, segmentation process, edge detection process, feature extraction process that is probability value of hand-shape chain code occurrence and process of database feature formation. In the second section is consist of hand-shape image acquisition process as BISINDO letters query followed by segmentation process, edge detection process, hand-shape feature extraction and recognition process by using calculation difference in distance between query shape feature to each shape feature in database feature. The image acquisition process in two parts above conducted directly (real time) via Webcam connected to the computer device. The method above has been implemented into prototype of Bisindo letters recognition software interface. The experiment results show the accuracy level of BISINDO letter recognition (26 BISINDO letters A to Z) which is reaching above 95%.

Unsupervised geodesic convex combination of shape dissimilarity measures

Dissimilarity or distance metrics are the cornerstone of shape matching and retrieval algorithms. As there is no unique dissimilarity measure that gives good performances in all possible configurations, these metrics are usually combined to provide reliable results. In this paper we propose to compute the best linear convex, or weighted, combination of any set of measured shape distances to enhance shape matching algorithms. To do so, a database is represented as a graph, where nodes are shapes and the edges carry the convex combination of dissimilarity measures. Weights are chosen to maximize the weighted distances between the query shape and shapes in the database. The optimal weights are solutions of a linear programming problem. This fully unsupervised method improves the outcomes of any set of shape similarity measures as shown in our experimental results performed on several popular 3D shape benchmarks.

LBO-Shape densities: A unified framework for 2D and 3D shape classification on the hypersphere of wavelet densities

Recent years have seen a sharp rise in shape classification applications, and following suit, several frameworks have been proposed for efficient indexing of shape models. Here we propose a state-of-the-art shape matching framework which concomitantly provides transformation invariance and computationally efficient querying. Shapes are represented as probability density functions estimated on the eigenspace of the shape’s Laplace–Beltrami operator (LBO) and the ensuing manifold geometry is leveraged to classify query shapes. Specifically, we estimate a nonparametric, square-root wavelet density on the low-order eigenvectors of the LBO, capturing the rich geometric structure of 2D and 3D shapes with very minimal pre-processing requirements. By estimating 3D, square-root wavelet densities on each shape’s eigenspace (LBO-Shape densities), both 2D and 3D shapes become identifiable with the unit hypersphere. Leveraging the hypersphere’s simple geometry, our avant-garde model-to-mean indexing scheme computes the intrinsic Karcher mean for each shape category, and then uses the closed-form distance between a query shape and the means to assign labels. In 2D, the need for burdensome preprocessing like extracting closed curves along with topological and equal point set cardinality requirements are eliminated. Similarly, in 3D we gain isometry invariance and rid ourselves of the need for superfluous feature extraction schemes. The extensive experimental results demonstrate that our approach is competitive with the state-of-the-art in 2D/3D shape matching algorithms.

Contextual Word Spotting in Historical Handwritten Documents

There are countless collections of historical documents in archives and libraries that contain plenty of valuable information for historians and researchers. The extraction of this information has become a central task among the Document Analysis researches and practitioners. There is an increasing interest to digital preserve and provide access to these kind of documents. But only the digitalization is not enough for the researchers. The extraction and/or indexation of information of this documents has had an increased interest among researchers. In many cases, and in particular in historical manuscripts, the full transcription of these documents is extremely difficult due the inherent deficiencies: poor physical preservation, different writing styles, obsolete languages, etc.Word spotting has become a popular an efficient alternative to full transcription. It inherently involves a high level of degradation in the images. The search of words is holistically formulated as a visual search of a given query shape in a larger image, instead of recognising the input text and searching the query word with an ascii string comparison. But the performance of classical word spotting approaches depend on the degradation level of the images being unacceptable in many cases . In this thesis we have proposed a novel paradigm called contextual word spotting method that uses the contextual/semantic information to achieve acceptable results whereas classical word spotting does not reach.The contextual word spotting framework proposed in this thesis is a segmentation-based word spotting approach, so an efficient word segmentation is needed. Historical handwritten documents present some common difficulties that can increase the difficulties the extraction of the words. We have proposed a line segmentation approach that formulates the problem as finding the central part path in the area between two consecutive lines. This is solved as a graph traversal problem. A path finding algorithm is used to find the optimal path in a graph, previously computed, between the text lines. Once the text lines are extracted, words are localized inside the text lines using a word segmentation technique from the state of the art.Classical word spotting approaches can be improved using the contextual information of the documents. We have introduced a new framework, oriented to handwritten documents that present a highly structure, to extract information making use of context. The framework is an efficient tool for semi-automatic transcription that uses the contextual information to achieve better results than classical word spotting approaches. The contextual information is automatically discovered by recognizing repetitive structures and categorizing all the words according to semantic classes. The most frequent words in each semantic cluster are extracted and the same text is used to transcribe all them.The experimental results achieved in this thesis outperform classical word spotting approaches demonstrating the suitability of the proposed ensemble architecture for spotting words in historical handwritten documents using contextual information.

Discrete Cosine Transformation and Height Functions Based Shape Representation and Classification

In this paper, we propose a combined classifier model based on two dimensional discrete cosine transform (2D-DCT) and Height Functions (HF) for accurate shape representation and classification. The DCT is capable of capturing the region information and Height Functions are insensitive to geometric transformations and nonlinear deformations. The Euclidean distance metric in case of DCT and Dynamic Programming (DP) in case of HF were respectively employed to obtain similarity values and hence fused to classify given query shape based on minimum similarity value. The experiments are conducted on publicly available shape datasets namely MPEG-7, Kimia-99, Kimia-216, Myth and Tools-2D and the results are presented by means of bull's eye score and precision-recall metric. The comparative study is also provided with the well known approaches to exhibit the retrieval accu- racy of the proposed approach. The experimental results demonstrate that the proposed approach yields significant improvement over some of the well known algorithms.

Polynomial local shape descriptor on interest points for 3D part-in-whole matching

Part-in-whole 3D shape matching is to recognize query shapes as sub-parts of a target intact 3D object. It plays a pivotal role in a large number of engineering applications. The most critical component in a part-in-whole search system is the local shape descriptor which encapsulates the identified local feature on the query part and is matched with the local shape descriptors of the parts in the database. We propose a novel local shape descriptor based on the concept that the evolution pattern of geodesic iso-contour’s length is a good representative for surface features. Our local shape descriptor enjoys a unique advantage over most existing ones by being sensitive to the geodesic radius of the local region, and thus is able to capture more comprehensive shape information if the query portion of the shape is larger and includes more complicated surface features. Through a simple approximation scheme, our local shape descriptor is defined as a vector piecewise polynomial function of the geodesic radius of the interest point, thus enabling local matching to be performed quickly by simple curve evaluations. We also introduce a new schema of interest points sampling so that we can reserve the most corresponding information of the model by a small number of local feature descriptors. The proposed part-in-whole matching approach outperforms many existing approaches in matching efficiency and requiring a smaller input region. It is a shortcut solution for incomplete model matching/retrieval.

Shape retrieval using hierarchical total Bregman soft clustering.

In this paper, we consider the family of total Bregman divergences (tBDs) as an efficient and robust "distance" measure to quantify the dissimilarity between shapes. We use the tBD-based ℓ₁-norm center as the representative of a set of shapes, and call it the t-center. First, we briefly present and analyze the properties of the tBDs and t-centers following our previous work in. Then, we prove that for any tBD, there exists a distribution which belongs to the lifted exponential family (lEF) of statistical distributions. Further, we show that finding the maximum a posteriori (MAP) estimate of the parameters of the lifted exponential family distribution is equivalent to minimizing the tBD to find the t-centers. This leads to a new clustering technique, namely, the total Bregman soft clustering algorithm. We evaluate the tBD, t-center, and the soft clustering algorithm on shape retrieval applications. Our shape retrieval framework is composed of three steps: 1) extraction of the shape boundary points, 2) affine alignment of the shapes and use of a Gaussian mixture model (GMM) to represent the aligned boundaries, and 3) comparison of the GMMs using tBD to find the best matches given a query shape. To further speed up the shape retrieval algorithm, we perform hierarchical clustering of the shapes using our total Bregman soft clustering algorithm. This enables us to compare the query with a small subset of shapes which are chosen to be the cluster t-centers. We evaluate our method on various public domain 2D and 3D databases, and demonstrate comparable or better results than state-of-the-art retrieval techniques.

Fast shape retrieval using a graph theoretic approach

A critical issue in shape retrieval systems is that when a user submits a query shape, some shapes in the database are returned relatively often, while some are returned only when submitting specific queries. Intuitively, this phenomenon yields suboptimal retrieval accuracy. In this paper, we address the shape retrieval problem by casting it into the task of identifying “authority” nodes in an inferred similarity graph and also by re-ranking the shapes. The main idea is that the average similarity between a node and its neighboring nodes takes into account the local distribution, and therefore, helps modify the neighborhood edge weight, which guides the re-ranking. The proposed approach is evaluated on both 2D and 3D shape datasets, and the experimental results show that the proposed neighborhood induced similarity measure significantly improves the shape retrieval performance. Moreover, the computational speed of the proposed method is extremely fast.

Theory and Practices in Xml Query Optimization

As computers and technology continue to become more commonplace and essential to everyday life, more data is captured, stored, and analyzed by a variety of institutions. As this amount of data grows, so does the need for efficient methodologies and tools used to store, retrieve, and transform the data. A common method used to store this schemaless, semi-structured data is through the Extensible Markup Language, XML. In this way, an XML document is viewed as a database. With this sizable amount of data stored in a common format, one problem is how to efficiently query XML documents. While relational database management systems contain built-in query optimizers, no such framework exists for XML databases. A multitude of document shapes, query shapes, index structures, and query techniques exist for XML databases, but the implications of these choices and their effects on query processing have not been investigated in a common framework. This paper focuses on the theory and practices applied to solve the problem of query optimization in XML databases. Keywords: Query Optimization, XML Databases, Cost Model, Query Optimization issues.

Co-Transduction for Shape Retrieval

In this paper, we propose a new shape/object retrieval algorithm, namely, co-transduction. The performance of a retrieval system is critically decided by the accuracy of adopted similarity measures (distances or metrics). In shape/object retrieval, ideally, intraclass objects should have smaller distances than interclass objects. However, it is a difficult task to design an ideal metric to account for the large intraclass variation. Different types of measures may focus on different aspects of the objects: for example, measures computed based on contours and skeletons are often complementary to each other. Our goal is to develop an algorithm to fuse different similarity measures for robust shape retrieval through a semisupervised learning framework. We name our method co-transduction, which is inspired by the co-training algorithm. Given two similarity measures and a query shape, the algorithm iteratively retrieves the most similar shapes using one measure and assigns them to a pool for the other measure to do a re-ranking, and vice versa. Using co-transduction, we achieved an improved result of 97.72% (bull's-eye measure) on the MPEG-7 data set over the state-of-the-art performance. We also present an algorithm called tri-transduction to fuse multiple-input similarities, and it achieved 99.06% on the MPEG-7 data set. Our algorithm is general, and it can be directly applied on input similarity measures/metrics; it is not limited to object shape retrieval and can be applied to other tasks for ranking/retrieval.

Prior Knowledge for Part Correspondence

AbstractClassical approaches to shape correspondence base their computation purely on the properties, in particular geometric similarity, of the shapes in question. Their performance still falls far short of that of humans in challenging cases where corresponding shape parts may differ significantly in geometry or even topology. We stipulate that in these cases, shape correspondence by humans involves recognition of the shape parts where prior knowledge on the parts would play a more dominant role than geometric similarity. We introduce an approach to part correspondence which incorporates prior knowledge imparted by a training set of pre‐segmented, labeled models and combines the knowledge with content‐driven analysis based on geometric similarity between the matched shapes. First, the prior knowledge is learned from the training set in the form of per‐label classifiers. Next, given two query shapes to be matched, we apply the classifiers to assign a probabilistic label to each shape face. Finally, by means of a joint labeling scheme, the probabilistic labels are used synergistically with pairwise assignments derived from geometric similarity to provide the resulting part correspondence. We show that the incorporation of knowledge is especially effective in dealing with shapes exhibiting large intra‐class variations. We also show that combining knowledge and content analyses outperforms approaches guided by either attribute alone.

Effective Improved Graph Transduction

In this paper, we focus on the problem of shape retrieval and clustering. We put two questions together because they are based on the same method, called Improved Graph Transduction. For shape retrieval, we regard the shape as a node in a graph and the similarity of shapes is represented by the edge of the graph. Then we learn a new distance measure between the query shape and the testing shapes. The main contribution of our work is to merge the most likely node with the query node during the learning process. The appending process helps us to mine the latent information in the propagation. The experimental results on the MPEG-7 data set show that comparing with the existing methods, our method can complete shape retrieval with similar correct rate in less time；For clustering task，the existing literatures in this domain often use the distance measure between the testing data point individual which is proved not enough in the real applications. In this paper, we think about the core concept in semi-supervised learning method, and use a graph to reflect the original distance measure, and combine the density information of the data distribution with the distance measure. Given a set of testing data, we select the original data randomly and use graph transduction iterative on the defined graph. The given algorithm is rapid and steady comparing with the existing clustering method. The experiments show that the novel algorithm is effective for the clustering task.