Representing and retrieving regions using binary partition trees

This paper discusses the interest of Binary Partition Trees for image and region representation in the context of indexing and similarity based retrieval. Binary Partition Trees concentrate in a compact and structured way the set of regions that compose an image. Since the tree is able to represent images in a multiresolution way, only simple descriptors need to be attached to the nodes. Moreover, this representation is used for similarity based region retrieval.Peer ReviewedPostprint (published version

How round is a protein? Exploring protein structures for globularity using conformal mapping.

We present a new algorithm that automatically computes a measure of the geometric difference between the surface of a protein and a round sphere. The algorithm takes as input two triangulated genus zero surfaces representing the protein and the round sphere, respectively, and constructs a discrete conformal map f between these surfaces. The conformal map is chosen to minimize a symmetric elastic energy E S (f) that measures the distance of f from an isometry. We illustrate our approach on a set of basic sample problems and then on a dataset of diverse protein structures. We show first that E S (f) is able to quantify the roundness of the Platonic solids and that for these surfaces it replicates well traditional measures of roundness such as the sphericity. We then demonstrate that the symmetric elastic energy E S (f) captures both global and local differences between two surfaces, showing that our method identifies the presence of protruding regions in protein structures and quantifies how these regions make the shape of a protein deviate from globularity. Based on these results, we show that E S (f) serves as a probe of the limits of the application of conformal mapping to parametrize protein shapes. We identify limitations of the method and discuss its extension to achieving automatic registration of protein structures based on their surface geometry

Shape-Based Plagiarism Detection for Flowchart Figures in Texts

Plagiarism detection is well known phenomenon in the academic arena. Copying other people is considered as serious offence that needs to be checked. There are many plagiarism detection systems such as turn-it-in that has been developed to provide this checks. Most, if not all, discard the figures and charts before checking for plagiarism. Discarding the figures and charts results in look holes that people can take advantage. That means people can plagiarized figures and charts easily without the current plagiarism systems detecting it. There are very few papers which talks about flowcharts plagiarism detection. Therefore, there is a need to develop a system that will detect plagiarism in figures and charts. This paper presents a method for detecting flow chart figure plagiarism based on shape-based image processing and multimedia retrieval. The method managed to retrieve flowcharts with ranked similarity according to different matching sets.Comment: 12 page

About the nature of Kansei information, from abstract to concrete

Designer’s expertise refers to the scientific fields of emotional design and kansei information. This paper aims to answer to a scientific major issue which is, how to formalize designer’s knowledge, rules, skills into kansei information systems. Kansei can be considered as a psycho-physiologic, perceptive, cognitive and affective process through a particular experience. Kansei oriented methods include various approaches which deal with semantics and emotions, and show the correlation with some design properties. Kansei words may include semantic, sensory, emotional descriptors, and also objects names and product attributes. Kansei levels of information can be seen on an axis going from abstract to concrete dimensions. Sociological value is the most abstract information positioned on this axis. Previous studies demonstrate the values the people aspire to drive their emotional reactions in front of particular semantics. This means that the value dimension should be considered in kansei studies. Through a chain of value-function-product attributes it is possible to enrich design generation and design evaluation processes. This paper describes some knowledge structures and formalisms we established according to this chain, which can be further used for implementing computer aided design tools dedicated to early design. These structures open to new formalisms which enable to integrate design information in a non-hierarchical way. The foreseen algorithmic implementation may be based on the association of ontologies and bag-of-words.AN

Shape matching by curve modelling and alignment

Automatic information retrieval in the eld of shape recognition has been widely covered by many research elds. Various techniques have been developed using different approaches such as intensity-based, modelbased and shape-based methods. Whichever is the way to represent the objects in images, a recognition method should be robust in the presence of scale change, translation and rotation. In this paper we present a new recognition method based on a curve alignment technique, for planar image contours. The method consists of various phases including extracting outlines of images, detecting signicant points and aligning curves. The dominant points can be manually or automatically detected. The matching phase uses the idea of calculating the overlapping indices between shapes as similarity measures. To evaluate the effectiveness of the algorithm, two databases of 216 and 99 images have been used. A performance analysis and comparison is provided by precision-recall curves

Efficient contour-based shape representation and matching

This paper presents an efficient method for calculating the similarity between 2D closed shape contours. The proposed algorithm is invariant to translation, scale change and rotation. It can be used for database retrieval or for detecting regions with a particular shape in video sequences. The proposed algorithm is suitable for real-time applications. In the first stage of the algorithm, an ordered sequence of contour points approximating the shapes is extracted from the input binary images. The contours are translation and scale-size normalized, and small sets of the most likely starting points for both shapes are extracted. In the second stage, the starting points from both shapes are assigned into pairs and rotation alignment is performed. The dissimilarity measure is based on the geometrical distances between corresponding contour points. A fast sub-optimal method for solving the correspondence problem between contour points from two shapes is proposed. The dissimilarity measure is calculated for each pair of starting points. The lowest dissimilarity is taken as the final dissimilarity measure between two shapes. Three different experiments are carried out using the proposed approach: letter recognition using a web camera, our own simulation of Part B of the MPEG-7 core experiment “CE-Shape1” and detection of characters in cartoon video sequences. Results indicate that the proposed dissimilarity measure is aligned with human intuition

PocketMatch: A new algorithm to compare binding sites in protein structures

Background: Recognizing similarities and deriving relationships among protein molecules is a fundamental
requirement in present-day biology. Similarities can be present at various levels which can be detected through comparison of protein sequences or their structural folds. In some cases similarities obscure at these levels could be present merely in the substructures at their binding sites. Inferring functional similarities between protein molecules by comparing their binding sites is still largely exploratory and not as yet a routine protocol. One of
the main reasons for this is the limitation in the choice of appropriate analytical tools that can compare binding sites with high sensitivity. To benefit from the enormous amount of structural data that is being rapidly accumulated, it is essential to have high throughput tools that enable large scale binding site comparison.

Results: Here we present a new algorithm PocketMatch for comparison of binding sites in a frame invariant
manner. Each binding site is represented by 90 lists of sorted distances capturing shape and chemical nature of the site. The sorted arrays are then aligned using an incremental alignment method and scored to obtain PMScores for pairs of sites. A comprehensive sensitivity analysis and an extensive validation of the algorithm have been carried out. Perturbation studies where the geometry of a given site was retained but the residue types were changed randomly, indicated that chance similarities were virtually non-existent. Our analysis also demonstrates that shape information alone is insufficient to discriminate between diverse binding sites, unless
combined with chemical nature of amino acids.

Conclusions: A new algorithm has been developed to compare binding sites in accurate, efficient and
high-throughput manner. Though the representation used is conceptually simplistic, we demonstrate that along
with the new alignment strategy used, it is sufficient to enable binding comparison with high sensitivity. Novel methodology has also been presented for validating the algorithm for accuracy and sensitivity with respect to geometry and chemical nature of the site. The method is also fast and takes about 1/250th second for one comparison on a single processor. A parallel version on BlueGene has also been implemented