The history of life can be viewed as a perpetual memory process (PMP) denoted as the pair (K, R), where K is knowledge information that is both shapeless and incorruptible, whereas R is the K realization with opposite properties. We are now faced with an emerging PMP for the huge quantity of human external knowledge, i.e., culture. In this paper, the author proposes that this new PMP should be the target of information systems science, and knowledge compression by the factorization of similar systems in diverse areas is one of its main tasks. In addition, this paper proposes a new academic discipline, semistry, that contributes to a solution side by side with the visual language EPN-PNUT, an extension of the Petri net, which represents our world by a set of local transition rules and tokens.

Locality-Sensitive Hashing (LSH) is a well-known approximate nearest-neighbor search algorithm for high-dimensional data. Though LSH searches nearest-neighbor points for a query very fast, LSH has a drawback that the space complexity is very large. For this reason, so as to apply LSH to a large dataset, it is crucial to implement LSH in distributed environments which consist of multiple nodes. One simple and natural method to implement LSH in the distributed environment is to have every node keep the same number of hash tables. However, this method increases remote accesses, because many nodes are accessed to access all the hash tables. Thus, this simple method will suffer from the long query response time, if the communication delay is the bottleneck. This paper proposes to reduce remote accesses by assigning hash buckets smartly to the nodes. In particular, our method assigns hash buckets from different hash tables to the same node, if the hash buckets store the same points. Due to this strategy, our method can access multiple hash buckets that should be accessed in processing a query with a single remote access, thereby decreasing remote accesses.

Video object segmentation is a task to distinguish the foreground from the background in videos. Most previous research on automatic video object segmentation based on graphcut segmentation uses the motion cue and the color cue to separate the background from the foreground. Consequently, the segmentation result deteriorates when the motion and/or the color becomes disordered, which typically occurs when a moving object stops and when a light is switched on/off. This paper proposes a new automatic video segmentation method robust to unstable motion and color. To achieve robustness, the graphcut segmentation is supported by the SURF feature, which is highly invariant to the change of scale, rotation, and luminance. In particular, our method matches the SURF features between two consecutive frames and modifies the segmentation result when the matched SURF features are assigned different labels.

This paper proposes PRDC-SSIS, a new compressibility-feature based semantic-sensitive image segmentation method using PRDC. One of the drawbacks of traditional signal (pixel-color) based image segmentation is the poor capability to capture the semantical information contained in the images. Because the semantic information tends to be carried by a set of neighboring pixels, rather than an individual pixel, we divide the image into patches and classify the patches based on their semantical contents. The crucial problem is classifying the patches into groups of similar patches according to their contents, and so we exploit the compressibility feature vector space of PRDC to accomplish this. An application of this method to an EO-image confirmed the proposed scheme can be carried out without any of the human-tailored target object models required by almost all traditional methods.

This paper introduces a new data analyzer, the compression-based self-organizing recognizer PRDC-CSOR, with a preliminary application to an EO-image. PRDC-CSOR is an application of the authors' previously proposed pattern representation scheme using data compression (PRDC). Contrary to the traditional statistical model based recognition system methods, PRDC-CSOR constructs itself by using incoming data only. The basic tool, compressibility, is an approximation of the Kolmogorov complexity K(x) defined on an individual text x as a countermeasure to the Shannon entropy H(X) defined on an ensemble X. Due to this feature, a highly automatic self-organizing recognition system becomes possible as shown in this paper.

Tree is a data structure used to express various objects such as semistructured data and genes. When objects are represented as trees, computing tree similarity is essential for pattern recognition and retrieval. This paper considers the noisy subsequence tree recognition problem whose purpose is to recognize the original tree, given its noisy subsequence tree. Previous research on this problem relied on constrained tree edit distance to measure the dissimilarity. However, the number of relabelings must be predetermined to compute it. This paper proposes a new dissimilarity measure for this problem. Our dissimilarity measure is obtained by counting the node edit operations included in the unit-cost tree edit distance that contribute to the matching of node labels. The number of relabelings need not be specified to compute our dissimilarity measure. Moreover, our measure achieves more accurate recognition performance and faster execution speed than the constrained tree edit distance. Our measure is also useful to solve the tree inclusion problem which is the problem of deciding whether a tree includes another tree and shows the extent of approximate tree inclusion when a tree incompletely includes another tree. © 2011 Wiley Periodicals, Inc.

This paper presents a novel approach to discovering particular objects from a set of unannotated images. We aim to find discriminative feature sets that can effectively represent particular object classes (as opposed to object categories). We achieve this by mining correlated visual word sets from the bag-of-features model. Specifically, we consider that a visual word set belongs to the same object class if all its visual words consistently occur together in the same image. To efficiently find such sets we apply Min-LSH to the occurrence vector of the each visual word. An agglomerative hierarchical clustering is further performed to eliminate redundancy and obtain more representative sets. We also propose a simple and efficient strategy for quantizing the feature descriptors based on locality-sensitive hashing. By experiment, we show that our approach can efficiently discover objects against cluster and slight viewpoint variations.

In MPEG standards, motion compensation is used for inter-frame compression. Motion compensation generates motion vectors in order to predict the current frame regions from previously decoded frames. These motion vectors represent motion information between regions in different frames and are useful in motion analysis. However, the motion vectors of homogeneous, low-textured, and line regions tend to be unstable and noisy; therefore, it becomes difficult to conduct motion analysis. In this paper, we propose a noisy motion vector reduction method for motion analysis using MPEG motion vectors by introducing a global motion estimation method and a zero-comparison method. The proposed method outputs local motion vectors that are free of noise and high-quality global motion vectors that can be used for object detection, tracking, etc. Further, this method works even for videos captured by a moving camera. We demonstrate the effectiveness of the proposed method through several experiments using actual videos.

In this paper, we propose a novel moving region detection method from the viewpoint of solving the transportation problem. This method extracts the relations between regions as a solution to the transformation problem between pixels belonging to adjacent frames. Moving regions are detected by utilizing the properties of these relations. This method does not require any models such as prior knowledge or particular assumptions about moving objects or backgrounds in a video. Since the method adaptively detects moving regions from input frame data, it can deal with the fluctuations of moving objects or backgrounds. We demonstrate the effectiveness of the proposed method through several experiments conducted using actual videos.

AQM is a technique for congestion control such that a router notifies congestion to a TCP sender when congestion occurs. Almost no AQM algorithms ever take the RTT values of TCP connections into account in congestion control, despite they are essential parameters. This paper proposes a new AQM algorithm that exploits them explicitly by introducing a passive RTT estimation technique in a router. The simulation results show that our AQM stabilizes the queue length better than previous AQM algorithms

In multi-robot system, communication is indispensable for effective cooperative working. In this system, direct communication by physical methods such as light, sound, radio wave is quite general. But in biological system, especially in the insect world, not only the physical but also the chemical communication methods can be observed. As the chemical methods have some unique properties, it is challenging to apply such a method to the cooperative multi-robot system. Unfortunately, to treat real chemical materials for the robots is not easy for now because of some technical difficulties. In this paper, we propose virtual pheromone system in which chemical signals are simulated with the graphics projected on the floor, and in which the robots decide their action depending on the color information of the graphics. We examined the performance of this system through the foraging task, which is one of the most popular tasks for multi-robot system and is generally observed in ant societies.

We introduce the concept of the ill-configured object (ICO). An ICO is a geometrical object having a stable (unique) name but varying configurations (shape, size, components, and component layout). In addition, we introduce the concept of the neighbour set representation (NSR) of an object, and show that the NSR is well-fitted to design an algorithm to detect ICOs in images. Two applications of the proposed theory to aerial image analysis are then presented.

Research of multirobot systems is active in these days. The most remarkable characteristic of multirobot system is that the robots work cooperatively and achieve the task which a single robot cannot do. This characteristic is important and it is essential to investigate an effect of number of robots in the system. There are some tasks which art! suitable for multirobot system and we chose foraging task. A robot used in this paper has a simple interaction method with a light signal. In this paper, we discuss their behavior in a field that has one feeding point at first. Their behavior is analyzed by a robot simulation and a mathematical model. Next, we discuss a performance of the system in a clockface arranged foraging field, in which some localized foods are located equidistant from the home. Here, we report the group shows an ordered behavior depending on the number of robots and the strength of the interaction.

We propose the PRDC (Pattern Representation based on Data Compression) scheme for media data analysis. PRDC is composed of two parts: an encoder that translates input data into text and a set of text compressors to generate a compression-ratio vector (CV). The CV is used as a feature of the input data. By preparing a set of media-specific encoders, PRDC becomes widely applicable. Analysis tasks - both categorization (class formation) and recognition (classification) - can be realized using CVs. After a mathematical discussion on the realizability of PRDC, the wide applicability of this scheme is demonstrated through the automatic categorization and/or recognition of music, voices, genomes, handwritten sketches and color images.

Protein has a complicated spatial structure and has chemical and physical functions that originate from the structure. It is important to predict the structure and functions of the protein from a DNA sequence or an amino acid sequence from a viewpoint of biology, medical science, protein engineering and so on. But at present no method is available to predict them accurately from the sequence. Instead, there are some approaches to estimate the functions approximately based on a similarity retrieval of sequences. We propose a new method for similarity retrieval of amino acid sequence based on the concept of homology retrieval using data compression. Introduction of the compression by dictionary technique enables us to describe the text data as an n-dimensional vector using n dictionaries which are generated by compressing n typical texts, and it also enables us to classify them based on their similarity. To classify the data clearly, it is effective to use ideal character strings as dictionaries. In this paper, we introduce genetic algorithm for dictionary generation and classify the amino acid sequences. Effectiveness of our proposal is examined using real genome data.