An efficient iconic indexing strategy for image rotation and reflection in image databases

Spatial relationships are important issues for similarity-based retrieval in many image database applications. With the popularity of digital cameras and the related image processing software, a sequence of images are often rotated or flipped. That is, those images are transformed in the rotation orientation or the reflection direction. However, many iconic indexing strategies based on symbolic projection are sensitive to rotation or reflection. Therefore, these strategies may miss the qualified images, when the query is issued in the orientation different from the orientation of the database images. To solve this problem, some researchers proposed a function to map the spatial relationship to its transformed one. However, this mapping consists of several conditional statements, which is time-consuming. Thus, in this paper, we propose an efficient iconic indexing strategy, in which we carefully assign a unique bit pattern to each spatial relationship and record the spatial information based on the bit patterns in a matrix. Without generating the rotated or flipped image, we can directly derive the index of the rotated or flipped image from the index of the original one by bit operations and matrix manipulation. In our performance study, we analyze the time complexity of our proposed strategy and show the efficiency of our proposed strategy according to the simulation results. Moreover, we implement a prototype to validate our proposed strategy.     

Effective product quantization-based indexing for nearest neighbor search

Multimedia Tools and Applications - Product quantization is a widely used lossy compression technique that can generate high quantization levels by a compact codebook set. It has been conducted in...     

BoostMap: an embedding method for efficient nearest neighbor retrieval

This paper describes BoostMap, a method for efficient nearest neighbor retrieval under computationally expensive distance measures. Database and query objects are embedded into a vector space, in which distances can be measured efficiently. Each embedding is treated as a classifier that predicts for any three objects X, A, B whether X is closer to A or to B. It is shown that a linear combination of such embeddingbased classifiers naturally corresponds to an embedding and a distance measure. Based on this property, the BoostMap method reduces the problem of embedding construction to the classical boosting problem of combining many weak classifiers into an optimized strong classifier. The classification accuracy of the resulting strong classifier is a direct measure of the amount of nearest neighbor structure preserved by the embedding. An important property of BoostMap is that the embedding optimization criterion is equally valid in both metric and non-metric spaces. Performance is evaluated in databases of hand images, handwritten digits, and time series. In all cases, BoostMap significantly improves retrieval efficiency with small losses in accuracy compared to brute-force search. Moreover, BoostMap significantly outperforms existing nearest neighbor retrieval methods, such as Lipschitz embeddings, FastMap, and VP-trees.     

An efficient k nearest neighbor search for multivariate time series

Multivariate time series (MTS) datasets are common in various multimedia, medical and financial applications. In order to efficiently perform k nearest neighbor searches for MTS datasets, we present a similarity measure, Eros (extended Frobenius norm), an index structure, Muse (multilevel distance-based index structure for Eros), and a feature subset selection technique, Ropes (recursive feature elimination on common principal components for Eros). Eros is based on principal component analysis, and computes the similarity between two MTS items by measuring how close the corresponding principal components are using the eigenvalues as weights. Muse constructs each level as a distance-based index structure without using the weights, up to z levels, which are combined at the query time with the weights. Ropes utilizes both the common principal components and the weights recursively in order to select a subset of features for Eros. The experimental results show the superiority of our techniques as compared to earlier approaches.     

Partially specified nearest neighbor searches using k-d trees

A partially specified nearest neighbor query is a nearest neighbor search in which only some of the possible keys are specified. An algorithm that uses k-d trees to perform such searching is described. The expected time complexity is O(N^{1-$\text{j}\text{k}$}). where k is the total number of keys and j the number of keys specified in the query. Experimental results, which are consistent with the theoretical predictions, are also presented.     

Handwritten character classification using nearest neighbor inlarge databases

Shows that systems built on a simple statistical technique and a large training database can be automatically optimized to produce classification accuracies of 99% in the domain of handwritten digits. It is also shown that the performance of these systems scale consistently with the size of the training database, where the error rate is cut by more than half for every tenfold increase in the size of the training set from 10 to 100,000 examples. Three distance metrics for the standard nearest neighbor classification system are investigated: a simple Hamming distance metric, a pixel distance metric, and a metric based on the extraction of penstroke features. Systems employing these metrics were trained and tested on a standard, publicly available, database of nearly 225,000 digits provided by the National Institute of Standards and Technology. Additionally, a confidence metric is both introduced by the authors and also discovered and optimized by the system. The new confidence measure proves to be superior to the commonly used nearest neighbor distance     

空间数据库中的障碍反向最近邻查询

反向最近邻查询已成为空间查询的热点问题,而障碍物在实际应用中是不可避免的,因而在障碍物环境中的反向最近邻查询也成为重要的空间查询。已有的可视反向最近邻查询只考虑了可视性,并没有考虑最小障碍距离。提出一种障碍物环境中新的反向最近邻查询的变体,查找障碍距离最小的反向最近邻,即障碍反向最近邻查询。利用障碍距离的计算和相应的剪枝规则,给出障碍反向最近邻查询的算法及相关定理和证明。     

An efficient LSH indexing on discriminative short codes for high-dimensional nearest neighbors

Multimedia Tools and Applications - In massive multimedia era, the dimension curse and the I/O performance bottleneck have become two major challenges for disk-based Approximate Nearest Neighbor...     

Continuous visible nearest neighbor query processing in spatial databases

In this paper, we identify and solve a new type of spatial queries, called continuous visible nearest neighbor (CVNN) search. Given a data set P, an obstacle set O, and a query line segment q in a two-dimensional space, a CVNN query returns a set of \({\langle p, R\rangle}\) tuples such that \({p \in P}\) is the nearest neighbor to every point r along the interval \({R \subseteq q}\) as well as p is visible to r. Note that p may be NULL, meaning that all points in P are invisible to all points in R due to the obstruction of some obstacles in O. In contrast to existing continuous nearest neighbor query, CVNN retrieval considers the impact of obstacles on visibility between objects, which is ignored by most of spatial queries. We formulate the problem, analyze its unique characteristics, and develop efficient algorithms for exact CVNN query processing. Our methods (1) utilize conventional data-partitioning indices (e.g., R-trees) on both P and O, (2) tackle the CVNN search by performing a single query for the entire query line segment, and (3) only access the data points and obstacles relevant to the final query result by employing a suite of effective pruning heuristics. In addition, several interesting variations of CVNN queries have been introduced, and they can be supported by our techniques, which further demonstrates the flexibility of the proposed algorithms. A comprehensive experimental evaluation using both real and synthetic data sets has been conducted to verify the effectiveness of our proposed pruning heuristics and the performance of our proposed algorithms.     

An efficient secure k nearest neighbor classification protocol with high-dimensional features

k Nearest neighbor (kNN) classification algorithm is a prediction model which is widely used for real-life applications, such as healthcare, finance, computer vision, personalization recommendation and precision marketing. The arrival of data explosion era results in the significant increase of feature dimension, which also makes for the increase of privacy concern over the available samples and unlabeled data in the applications of machine learning. In this paper, we present a secure low communication overhead kNN classification protocol that is able to deal with high-dimensional features given in real numbers. First, to deal with feature values given in real numbers, we develop a specific data conversion algorithm, which is used in the chosen fully homomorphic scheme. This conversion algorithm is generic and applicable to other algorithms that need to handle real numbers using the fully homomorphic scheme. Second, we present a privacy-preserving euclidean distance protocol (PPEDP), which works with the Euclidean distance computation between two points given in real numbers in a high-dimensional space. Then, based on the novelty PPEDP and oblivious transfer, we propose a new classification approach, efficient secure kNN classification protocol, (ESkNN) with low communication overhead, which is appropriate for a sample set with high-dimensional features and real number feature values. Moreover, we implement ESkNN in C++. Experimental results show that ESkNN is several orders of magnitude faster in performance than existing works, and scales up to 18 000 feature dimension in a memory limited environment.     

Color indexing for efficient image retrieval

Content based image retrieval is an active area of research. Many approaches have been proposed to retrieve images based on matching of some features derived from the image content. Color is an important feature of image content. The problem with many traditional matching-based retrieval methods is that the search time for retrieving similar images for a given query image increases linearly with the size of the image database. We present an efficient color indexing scheme for similarity-based retrieval which has a search time that increases logarithmically with the database size.In our approach, the color features are extracted automatically using a color clustering algorithm. Then the cluster centroids are used as representatives of the images in 3-dimensional color space and are indexed using a spatial indexing method that usesR-tree. The worst case search time complexity of this approach isOn _q log(N^* n_avg)), whereN is the number of images in the database, andn _q andn _avg are the number of colors in the query image and the average number of colors per image in the database respectively. We present the experimental results for the proposed approach on two databases consisting of 337 Trademark images and 200 Flag images.     

Graph-based image segmentation using directional nearest neighbor graph

Graph-based image segmentation techniques generally represent the problem in terms of a graph. In this work, we present a novel graph, called the directional nearest neighbor graph. The construction principle of this graph is that each node corresponding to a pixel in the image is connected to a fixed number of nearest neighbors measured by color value and the connected neighbors are distributed in four directions. Compared with the classical grid graph and the nearest neighbor graph, our method can capture low-level texture information using a less-connected edge topology. To test the performance of the proposed method, a comparison with other graph-based methods is carried out on synthetic and real-world images. Results show an improved segmentation for texture objects as well as a lower computational load.     

An optimal global nearest neighbor metric

A quadratic metric dAO (X, Y) =[(X - Y)T AO(X - Y)]? is proposed which minimizes the mean-squared error between the nearest neighbor asymptotic risk and the finite sample risk. Under linearity assumptions, a heuristic argument is given which indicates that this metric produces lower mean-squared error than the Euclidean metric. A nonparametric estimate of Ao is developed. If samples appear to come from a Gaussian mixture, an alternative, parametrically directed distance measure is suggested for nearness decisions within a limited region of space. Examples of some two-class Gaussian mixture distributions are included.     

An experimental comparison of clustering methods for content-based indexing of large image databases

In recent years, the expansion of acquisition devices such as digital cameras, the development of storage and transmission techniques of multimedia documents and the development of tablet computers facilitate the development of many large image databases as well as the interactions with the users. This increases the need for efficient and robust methods for finding information in these huge masses of data, including feature extraction methods and feature space structuring methods. The feature extraction methods aim to extract, for each image, one or more visual signatures representing the content of this image. The feature space structuring methods organize indexed images in order to facilitate, accelerate and improve the results of further retrieval. Clustering is one kind of feature space structuring methods. There are different types of clustering such as hierarchical clustering, density-based clustering, grid-based clustering, etc. In an interactive context where the user may modify the automatic clustering results, incrementality and hierarchical structuring are properties growing in interest for the clustering algorithms. In this article, we propose an experimental comparison of different clustering methods for structuring large image databases, using a rigorous experimental protocol. We use different image databases of increasing sizes (Wang, PascalVoc2006, Caltech101, Corel30k) to study the scalability of the different approaches.     

An efficient compressed domain video indexing method

Video indexing is employed to represent the features of video sequences. Motion vectors derived from compressed video are preferred for video indexing because they can be accessed by partial decoding; thus, they are used extensively in various video analysis and indexing applications. In this study, we introduce an efficient compressed domain video indexing method and implement it on the H.264/AVC coded videos. The video retrieval experimental evaluations indicate that the video retrieval based on the proposed indexing method outperforms motion vector based video retrieval in 74 % of queries with little increase in computation time. Furthermore, we compared our method with a pixel level video indexing method which employs both temporal and spatial features. Experimental evaluation results indicate that our method outperforms the pixel level method both in performance and speed. Hence considering the speed and precision characteristics of indexing methods, the proposed method is an efficient indexing method which can be used in various video indexing and retrieval applications.     

Probabilistic cost model for nearest neighbor search in image retrieval

We present a probabilistic cost model to analyze the performance of the kd-tree for nearest neighbor search in the context of content-based image retrieval. Our cost model measures the expected number of kd-tree nodes traversed during the search query. We show that our cost model has high correlations with both the observed number of traversed nodes and the runtime performance of search queries used in image retrieval. Furthermore, we prove that, if the query points follow the distribution of data used to construct the kd-trees, the median-based partitioning method as well as PCA-based partitioning technique can produce near-optimal kd-trees in terms of minimizing our cost model. The probabilistic cost model is validated through experiments in SIFT-based image retrieval.     

Overlap pattern synthesis with an efficient nearest neighbor classifier

Nearest neighbor (NN) classifier is the most popular non-parametric classifier. It is a simple classifier with no design phase and shows good performance. Important factors affecting the efficiency and performance of NN classifier are (i) memory required to store the training set, (ii) classification time required to search the nearest neighbor of a given test pattern, and (iii) due to the curse of dimensionality the number of training patterns needed by it to achieve a given classification accuracy becomes prohibitively large when the dimensionality of the data is high. In this paper, we propose novel techniques to improve the performance of NN classifier and at the same time to reduce its computational burden. These techniques are broadly based on: (i) overlap based pattern synthesis which can generate a larger number of artificial patterns than the number of input patterns and thus can reduce the curse of dimensionality effect, (ii) a compact representation of the given set of training patterns called overlap pattern graph (OLP-graph) which can be incrementally built by scanning the training set only once and (iii) an efficient NN classifier called OLP-NNC which directly works with OLP-graph and does implicit overlap based pattern synthesis. A comparison based on experimental results is given between some of the relevant classifiers. The proposed schemes are suitable for applications dealing with large and high dimensional datasets like those in data mining.     

K nearest neighbor reinforced expectation maximization method

K nearest neighbor and Bayesian methods are effective methods of machine learning. Expectation maximization is an effective Bayesian classifier. In this work a data elimination approach is proposed to improve data clustering. The proposed method is based on hybridization of k nearest neighbor and expectation maximization algorithms. The k nearest neighbor algorithm is considered as the preprocessor for expectation maximization algorithm to reduce the amount of training data making it difficult to learn. The suggested method is tested on well-known machine learning data sets iris, wine, breast cancer, glass and yeast. Simulations are done in MATLAB environment and performance results are concluded.     

An efficient cost model for optimization of nearest neighbor search in low and medium dimensional spaces

Existing models for nearest neighbor search in multidimensional spaces are not appropriate for query optimization because they either lead to erroneous estimation or involve complex equations that are expensive to evaluate in real-time. This article proposes an alternative method that captures the performance of nearest neighbor queries using approximation. For uniform data, our model involves closed formulae that are very efficient to compute and accurate for up to 10 dimensions. Further, the proposed equations can be applied on nonuniform data with the aid of histograms. We demonstrate the effectiveness of the model by using it to solve several optimization problems related to nearest neighbor search.