Optimized Block-based Connected Components Labeling with Decision Trees

In this paper we define a new paradigm for 8-connection labeling, which employes a general approach to improve neighborhood exploration and minimizes the number of memory accesses. Firstly we exploit and extend the decision table formalism introducing OR-decision tables, in which multiple alternative actions are managed. An automatic procedure to synthesize the optimal decision tree from the decision table is used, providing the most effective conditions evaluation order. Secondly we propose a new scanning technique that moves on a 2x2 pixel grid over the image, which is optimized by the automatically generated decision tree.An extensive comparison with the state of art approaches is proposed, both on synthetic and real datasets. The synthetic dataset is composed of different sizes and densities random images, while the real datasets are an artistic image analysis dataset, a document analysis dataset for text detection and recognition, and finally a standard resolution dataset for picture segmentation tasks. The algorithm provides an impressive speedup over the state of the art algorithms

Miniature illustrations retrieval and innovative interaction for digital illuminated manuscripts

In this paper we propose a multimedia solution for the interactive exploration of illuminated manuscripts. We leveraged on the joint exploitation of content-based image retrieval and relevance feedback to provide an effective mechanism to navigate through the manuscript and add custom knowledge in the form of tags. The similarity retrieval between miniature illustrations is based on covariance descriptors, integrating color, spatial and gradient information. The proposed relevance feedback technique, namely Query Remapping Feature Space Warping, accounts for the user’s opinions by accordingly warping the data points. This is obtained by means of a remapping strategy (from the Riemannian space where covariance matrices lie, referring back to Euclidean space) useful to boost the retrieval performance. Experiments are reported to show the quality of the proposal. Moreover, the complete prototype with user interaction, as already showcased at museums and exhibitions, is presented

Optimal Decision Trees for Local Image Processing Algorithms

In this paper we present a novel algorithm to synthesize an optimal decision tree from OR-decision tables, an extension of standard decision tables, complete with the formal proof of optimality and computational cost analysis. As many problems which require to recognize particular patterns can be modeled with this formalism, we select two common binary image processing algorithms, namely connected components labeling and thinning, to show how these can be represented with decision tables, and the benets of their implementation as optimal decision trees in terms of reduced memory accesses. Experiments are reported, to show the computational time improvements over state of the art implementations

2D Images Map Warping for Improved User Interaction

Abstract In this paper, we suggest an interaction model designed to fit users' expectations in front of an image retrieval system. A lightweight relevance feedback strategy, working directly on the 2D projection of image features, allows the user to spatially navigate the media collection maintaining the real-time constraint. A preliminary evaluation of this relevance feedback strategy shows good performance compared with other known approaches

The relationship between processing speed and regional white matter volume in healthy young people

Processing speed is considered a key cognitive resource and it has a crucial role in all types of cognitive performance. Some researchers have hypothesised the importance of white matter integrity in the brain for processing speed; however, the relationship at the whole-brain level between white matter volume (WMV) and processing speed relevant to the modality or problem used in the task has never been clearly evaluated in healthy people. In this study, we used various tests of processing speed and Voxel-Based Morphometry (VBM) analyses, it is involves a voxel-wise comparison of the local volume of gray and white, to assess the relationship between processing speed and regional WMV (rWMV). We examined the association between processing speed and WMV in 887 healthy young adults (504 men and 383 women; mean age, 20.7 years, SD, 1.85). We performed three different multiple regression analyses: we evaluated rWMV associated with individual differences in the simple processing speed task, word–colour and colour–word tasks (processing speed tasks with words) and the simple arithmetic task, after adjusting for age and sex. The results showed a positive relationship at the whole-brain level between rWMV and processing speed performance. In contrast, the processing speed performance did not correlate with rWMV in any of the regions examined. Our results support the idea that WMV is associated globally with processing speed performance regardless of the type of processing speed task

Optimal decision tree synthesis for efficient neighborhood computation

This work proposes a general approach to optimize the time required to perform a choice in a decision support system, with particular reference to image processing tasks with neighborhood analysis. The decisions are encoded in a decision table paradigm that allows multiple equivalent procedures to be performed for the same situation. An automatic synthesis of the optimal decision tree is implemented in order to generate the most efficient order in which conditions should be considered to minimize the computational requirements.To test out approach, the connected component labeling scenario is considered. Results will show the speedup introduced using an automatically built decision system able to efficiently analyze and explore the neighborhood

Relevance Feedback as an Interactive Navigation Tool

Image collections are searched in common retrieval systems in many different ways, but the typical presentation is by means of a grid styled view. In this paper we try to suggest a novel use of relevance feedback as a tool to warp the view and allow the user to spatially navigate the image collection, and at the same time focus on his retrieval aim. This is obtained by the use of a distance based space warping on the 2D projection of the distance matrix

Describing Texture Directions with Von Mises Distributions

In this work we describe a new approach for texture characterization. Starting from the autocorrelation matrix an elegant description through a mixture of Von Mises distributions is proposed. A compact 6 valued descriptor is produced for each block and served as input to an SVM classifier. Tests are carried out on high resolution illuminated manuscripts images