Color constant color indexing

Objects can be recognized on the basis of their color alone by color indexing, a technique developed by Swain-Ballard (1991) which involves matching color-space histograms. Color indexing fails, however, when the incident illumination varies either spatially or spectrally. Although this limitation might be overcome by preprocessing with a color constancy algorithm, we instead propose histogramming color ratios. Since the ratios of color RGB triples from neighboring locations are relatively insensitive to changes in the incident illumination, this circumvents the need for color constancy preprocessing. Results of tests with the new color-constant-color-indexing algorithm on synthetic and real images show that it works very well even when the illumination varies spatially in its intensity and color     

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.     

Two-dimensional substring indexing

As databases have expanded in scope to storing string data (XML documents, product catalogs), it has become increasingly important to search databases based on matching substrings, often on multiple, correlated dimensions. While string B-trees are I/O optimal in one dimension, no index structure with non-trivial query bounds is known for two-dimensional substring indexing. In this paper, we present a technique for two-dimensional substring indexing based on a reduction to the geometric problem of identifying common colors in two ranges containing colored points. We develop an I/O efficient algorithm for solving the common colors problem, and use it to obtain an I/O efficient (poly-logarithmic query time) algorithm for the two-dimensional substring indexing problem. Our techniques result in a family of secondary memory index structures that trade space for time, with no loss of accuracy.     

Experiments with discrimination-tree indexing and path indexing for term retrieval

This article addresses the problem of indexing and retrieving first-order predicate calculus terms in the context of automated deduction programs. The four retrieval operations of concern are to find variants, generalizations, instances, and terms that unify with a given term. Discrimination-tree indexing is reviewed, and several variations are presented. The path-indexing method is also reviewed. Experiments were conducted on large sets of terms to determine how the properties of the terms affect the performance of the two indexing methods. Results of the experiments are presented.This was supported by the Applied Mathematical Sciences subprogram of the Office of Energy Research, U.S. Department of Energy, under Contract W-31-109-Eng-38.     

Graph-based shape indexing

Graphs have become growingly important in representing shapes in computer vision. Given a query graph, it is essential to retrieve similar database graphs efficiently from a large database. In this paper, we present a graph-based indexing technique which overcomes significant drawbacks of the previous work (Demirci et al. in Comput Vis Image Underst 110(3):312–325, 2008) using a recently developed theorem from the domain of matrix analysis. Our technique starts by representing the topological structure of a graph in a vector space. As done in the previous work, the topological structure of a graph is constructed using its Laplacian spectra. However, unlike the previous approach, which represents all sugraphs of a database graph in the vector space to account for local similarity, a database graph in the proposed framework is represented as a single vector. By performing a range search around the query, the proposed indexing technique returns a set with both partial and global similarity. Empirical evaluation of the algorithm on an extensive set of retrieval trials including a comparison with the previous approach in both 2D and 3D demonstrates the effectiveness, efficiency, and robustness of the overall approach.     

Online timestamped text indexing

In this paper we present an efficient method to index a text stream on-line in a fashion that allows, at any point in time, to find the longest suffix of the text that has appeared previously, and the closest (farthest) times in which it has appeared. Our algorithms allow efficient answers to these queries.     

Pattern recognition by indexing

We consider a set-theoretic approach to pattern recognition and introduce the definition of an index for patterns and inverse patterns. Unlike traditional recognition methods, in our approach pattern identification reduces to operations with inverse patterns. We show that these novel notions let us significantly reduce the computational load by replacing pattern comparison operations with a much smaller number of inverse pattern intersection operations.     

Quantized ranking for permutation-based indexing

The K-Nearest Neighbor (K-NN) search problem is the way to find the K closest and most similar objects to a given query. The K-NN is essential for many applications such as information retrieval and visualization, machine learning and data mining. The exponential growth of data imposes to find approximate approaches to this problem. Permutation-based indexing is one of the most recent techniques for approximate similarity search. Objects are represented by permutation lists ordering their distances to a set of selected reference objects, following the idea that two neighboring objects have the same surrounding. In this paper, we propose a novel quantized representation of permutation lists with its related data structure for effective retrieval on single and multicore architectures. Our novel permutation-based indexing strategy is built to be fast, memory efficient and scalable. This is experimentally demonstrated in comparison to existing proposals using several large-scale datasets of millions of documents and of different dimensions.     

Random indexing of multidimensional data

Random indexing (RI) is a lightweight dimension reduction method, which is used, for example, to approximate vector semantic relationships in online natural language processing systems. Here we generalise RI to multidimensional arrays and therefore enable approximation of higher-order statistical relationships in data. The generalised method is a sparse implementation of random projections, which is the theoretical basis also for ordinary RI and other randomisation approaches to dimensionality reduction and data representation. We present numerical experiments which demonstrate that a multidimensional generalisation of RI is feasible, including comparisons with ordinary RI and principal component analysis. The RI method is well suited for online processing of data streams because relationship weights can be updated incrementally in a fixed-size distributed representation, and inner products can be approximated on the fly at low computational cost. An open source implementation of generalised RI is provided.     

Nonchronological video synopsis and indexing

The amount of captured video is growing with the increased numbers of video cameras, especially the increase of millions of surveillance cameras that operate 24 hours a day. Since video browsing and retrieval is time consuming, most captured video is never watched or examined. Video synopsis is an effective tool for browsing and indexing of such a video. It provides a short video representation, while preserving the essential activities of the original video. The activity in the video is condensed into a shorter period by simultaneously showing multiple activities, even when they originally occurred at different times. The synopsis video is also an index into the original video by pointing to the original time of each activity. Video Synopsis can be applied to create a synopsis of an endless video streams, as generated by webcams and by surveillance cameras. It can address queries like "Show in one minute the synopsis of this camera broadcast during the past day'. This process includes two major phases: (i) An online conversion of the endless video stream into a database of objects and activities (rather than frames). (ii) A response phase, generating the video synopsis as a response to the user's query.     

Shape indexing using self-organizing maps

In this paper, we propose a novel approach to generate the topology-preserving mapping of structural shapes using self-organizing maps (SOMs). The structural information of the geometrical shapes is captured by relational attribute vectors. These vectors are quantised using an SOM. Using this SOM, a histogram is generated for every shape. These histograms are treated as inputs to train another SOM which yields a topology-preserving mapping of the geometric shapes. By appropriately choosing the relational vectors, it is possible to generate a mapping that is invariant to some chosen transformations, such as rotation, translation, scale, affine, or perspective transformations. Experimental results using trademark objects are presented to demonstrate the performance of the proposed methodology.     

Content-based indexing of multimedia databases

Content-based retrieval of multimedia database calls for content-based indexing techniques. Different from conventional databases, where data items are represented by a set of attributes of elementary data types, multimedia objects in multimedia databases are represented by a collection of features; similarity of object contents depends on context and frame of reference; and features of objects are characterized by multimodal feature measures. These lead to great challenges for content-based indexing. On the other hand, there are special requirements on content-based indexing: to support visual browsing, similarity retrieval, and fuzzy retrieval, nodes of the index should represent certain meaningful categories. That is to say that certain semantics must be added when performing indexing. ContIndex, the context-based indexing technique presented in this paper, is proposed to meet these challenges and special requirements. The indexing tree is formally defined by adapting a classification-tree concept. Horizontal links among nodes in the same level enhance the flexibility of the index. A special neural-network model, called Learning based on Experiences and Perspectives (FEP), has been developed to create node categories by fusing multimodal feature measures. It brings into the index the capability of self-organizing nodes with respect to certain context and frames of reference. An icon image is generated for each intermediate node to facilitate visual browsing. Algorithms have been developed to support multimedia object archival and retrieval using Contlndex     

基于小波的压缩图像索引技术

在JPEG2000压缩框架下给出了两种压缩图像索引方法, 不需要完全解压缩, 减少了数据处理量。实验结果证明,给出的索引方法具有很强的图像表征能力,利用该索引进行图像检索,提高了检索效率。     

Efficient indexing for semantic search

The increasing performance and wider spread use of automated semantic annotation and entity linking platforms has empowered the possibility of using semantic information in information retrieval. While keyword-based information retrieval techniques have shown impressive performance, the addition of semantic information can increase retrieval performance by allowing for more accurate sense disambiguation, intent determination, and instance identification, just to name a few. Researchers have already delved into the possibility of integrating semantic information into practical search engines using a combination of techniques such as using graph databases, hybrid indices and adapted inverted indices, among others. One of the challenges with the efficient design of a search engine capable of considering semantic information is that it would need to be able to index information beyond the traditional information stored in inverted indices, including entity mentions and type relationships. The objective of our work in this paper is to investigate various ways in which different data structure types can be adopted to integrate three types of information including keywords, entities and types. We will systematically compare the performance of the different data structures for scenarios where (i) the same data structure types are adopted for the three types of information, and (ii) different data structure types are integrated for storing and retrieving the three different information types. We report our findings in terms of the performance of various query processing tasks such as Boolean and ranked intersection for the different indices and discuss which index type would be appropriate under different conditions for semantic search.     

Wavelet-based video indexing and querying

We present several algorithms suitable for analysis of broadcast video. First, we show how wavelet analysis of frames of video can be used to detect transitions between shots in a video stream, thereby dividing the stream into segments. Next we describe how each segment can be inserted into a video database using an indexing scheme that involves a wavelet-based “signature.” Finally, we show that during a subsequent broadcast of a similar or identical video clip, the segment can be found in the database by quickly searching for the relevant signature. The method is robust against noise and typical variations in the video stream, even global changes in brightness that can fool histogram-based techniques. In the paper, we compare experimentally our shot transition mechanism to a color histogram implementation, and also evaluate the effectiveness of our database-searching scheme. Our algorithms are very efficient and run in realtime on a desktop computer. We describe how this technology could be employed to construct a “smart VCR” that was capable of alerting the viewer to the beginning of a specific program or identifying     

Polyhedral object recognition by indexing

In computer vision, the indexing problem is the problem of recognizing a few objects in a large database of objects while avoiding the help of the classical image-feature-to-object-feature matching paradigm. In this paper we address the problem of recognizing three-dimensional (3-D) polyhedral objects from 2-D images by indexing. Both the objects to be recognized and the images are represented by weighted graphs. The indexing problem is therefore the problem of determining whether a graph extracted from the image is present or absent in a database of model graphs. We introduce a novel method for performing this graph indexing process which is based both on polynomial characterization of binary and weighted graphs and on hashing. We describe in detail this polynomial characterization and then we show how it can be used in the context of polyhedral object recognition. Next we describe a practical recognition-by-indexing system that includes the organization of the database, the representation of polyhedral objects in terms of 2-D characteristic views, the representation of this views in terms of weighted graphs and the associated image processing. Finally, some experimental results allow the evaluation of the system performance.     

适应性阈值优化的微博消息索引模式

为提高微博搜索的准确性,提出一种适应性的微博消息索引模式。将微博消息的转发和回复表示为树形结构并进行编码;提出一种基于内容和排名的索引模式,根据新消息的到来适应性地调整内存中的索引数据;为避免检索过程扫描整个微博数据集,提出一种Top-k阈值优化方法。Twitter数据实验结果表明,该模式降低了微博数据索引时的时间和空间开销,其性能随着时间的推移比较稳定。     

Transactional support for adaptive indexing

Adaptive indexing initializes and optimizes indexes incrementally, as a side effect of query processing. The goal is to achieve the benefits of indexes while hiding or minimizing the costs of index creation. However, index-optimizing side effects seem to turn read-only queries into update transactions that might, for example, create lock contention. This paper studies concurrency control and recovery in the context of adaptive indexing. We show that the design and implementation of adaptive indexing rigorously separates index structures from index contents; this relaxes constraints and requirements during adaptive indexing compared to those of traditional index updates. Our design adapts to the fact that an adaptive index is refined continuously and exploits any concurrency opportunities in a dynamic way. A detailed experimental analysis demonstrates that (a) adaptive indexing maintains its adaptive properties even when running concurrent queries, (b) adaptive indexing can exploit the opportunity for parallelism due to concurrent queries, (c) the number of concurrency conflicts and any concurrency administration overheads follow an adaptive behavior, decreasing as the workload evolves and adapting to the workload needs.     

Predicate-based indexing for desktop search

Google and other products have revolutionized the way we search for information. There are, however, still a number of research challenges. One challenge that arises specifically in desktop search is to exploit the structure and semantics of documents, as defined by the application program that generated the data (e.g., Word, Excel, or Outlook). The current generation of search products does not understand these structures and therefore often returns wrong results. This paper shows how today’s search technology can be extended in order to take the specific semantics of certain structures into account. The key idea is to extend inverted file index structures with predicates which encode the circumstances under which certain keywords of a document become visible to a user. This paper provides a framework that allows to express the semantics of structures in documents and algorithms to construct enhanced, predicate-based indexes. Furthermore, this paper shows how keyword and phrase queries can be processed efficiently on such enhanced indexes. It is shown that the proposed approach has superior retrieval performance with regard to both recall and precision and has tolerable space and query running time overheads.