Extending beyond the boundaries of science, art, and culture, content-based multimedia information retrieval provides new paradigms and methods for searching through the myriad variety of media all over the world. This survey reviews 100p recent articles on content-based multimedia information retrieval and discusses their role in current research directions which include browsing and search paradigms, user studies, affective computing, learning, semantic queries, new features and media types, high performance indexing, and evaluation techniques. Based on the current state of the art, we discuss the major challenges for the future.

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Content-based multimedia information retrieval: State of the art and challenges

We investigate techniques for analysis and retrieval of object trajectories in two or three dimensional space. Such data usually contain a large amount of noise, that has made previously used metrics fail. Therefore, we formalize non-metric similarity functions based on the longest common subsequence (LCSS), which are very robust to noise and furthermore provide an intuitive notion of similarity between trajectories by giving more weight to similar portions of the sequences. Stretching of sequences in time is allowed, as well as global translation of the sequences in space. Efficient approximate algorithms that compute these similarity measures are also provided. We compare these new methods to the widely used Euclidean and time warping distance functions (for real and synthetic data) and show the superiority of our approach, especially in the strong presence of noise. We prove a weaker version of the triangle inequality and employ it in an indexing structure to answer nearest neighbor queries. Finally, we present experimental results that validate the accuracy and efficiency of our approach.

/pdf/discovering-similar-multidimensional-trajectories-3ofj6nb9yv.pdf

Discovering similar multidimensional trajectories

Utility Analysis and the Consumption Function: An Interpretation of Cross-Section Data

Computational geometry

The coming years will witness dramatic advances in wireless communications as well as positioning technologies. As a result, tracking the changing positions of objects capable of continuous movement is becoming increasingly feasible and necessary. The present paper proposes a novel, R*-tree based indexing technique that supports the efficient querying of the current and projected future positions of such moving objects. The technique is capable of indexing objects moving in one-, two-, and three-dimensional space. Update algorithms enable the index to accommodate a dynamic data set, where objects may appear and disappear, and where changes occur in the anticipated positions of existing objects. A comprehensive performance study is reported.

/pdf/indexing-the-positions-of-continuously-moving-objects-3eaflrmi98.pdf

Indexing the positions of continuously moving objects

Indexing the Positions of Continuously Moving Objects.

With the proliferation of wireless communications and the rapid advances in technologies for tracking the positions of continuously moving objects, algorithms for efficiently answering queries about large numbers of moving objects increasingly are needed. One such query is the reverse nearest neighbor (RNN) query that returns the objects that have a query object as their closest object. While algorithms have been proposed that compute RNN queries for non-moving objects, there have been no proposals for answering RNN queries for continuously moving objects. Another such query is the nearest neighbor (NN) query, which has been studied extensively and in many contexts. Like the RNN query, the NN query has not been explored for moving query and data points. This paper proposes an algorithm for answering RNN queries for continuously moving points in the plane. As a part of the solution to this problem and as a separate contribution, an algorithm for answering NN queries for continuously moving points is also proposed. The results of performance experiments are reported.

/pdf/nearest-neighbor-and-reverse-nearest-neighbor-queries-for-4t6lv31yyw.pdf

Nearest neighbor and reverse nearest neighbor queries for moving objects

Visionaries predict that the Internet will soon extend to billions of wireless devices, or objects, a substantial fraction of which will offer their changing positions to location-based services. This paper assumes an Internet-service scenario where objects that have not reported their position within a specified duration of time are expected to no longer be interested in, or of interest to, the service. Due to the possibility of many "expiring" objects, a highly dynamic database results. The paper presents an R-tree based technique for the indexing of the current positions of such objects. Different types of bounding regions are studied, and new algorithms are provided for maintaining the tree structure. Performance experiments indicate that, when compared to the approach where the objects are not assumed to expire, the new indexing technique can improve search performance by a factor of two or more without sacrificing update performance.

Indexing of moving objects for location-based services

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Papers

Indexing the positions of continuously moving objects

Indexing the Positions of Continuously Moving Objects.

Nearest neighbor and reverse nearest neighbor queries for moving objects

Indexing of moving objects for location-based services

Advances in Database Technology — EDBT 2002