Ignazio Gallo

A cognitive pyramid for contextual classification of remote sensing images

Many cases of remote sensing classification present complicated patterns that cannot be identified on the basis of spectral data alone, but require contextual methods that base class discrimination on the spatial relationships between the individual pixel and local and global configurations of neighboring pixels. However, the use of contextual classification is still limited by critical issues, such as complexity and problem dependency. We propose here a contextual classification strategy for object recognition in remote sensing images in an attempt to solve recognition tasks operatively. The salient characteristics of the strategy are the definition of a multiresolution feature extraction procedure exploiting human perception and the use of soft neural classification based on the multilayer perceptron model. Three experiments were conducted to evaluate the performance of the methodology, one in an easily controlled domain using synthetic images, the other two in real domains involving builtup pattern recognition in panchromatic aerial photographs and high-resolution satellite images.

An online document clustering technique for short web contents

Document clustering techniques have been applied in several areas, with the web as one of the most recent and influential. Both general-purpose and text-oriented techniques exist and can be used to cluster a collection of documents in many ways. This work proposes a novel heuristic online document clustering model that can be specialized with a variety of text-oriented similarity measures. An experimental evaluation of the proposed model was conducted in the e-commerce domain. Performances were measured using a clustering-oriented metric based on F-Measure and compared with those obtained by other well-known approaches. The obtained results confirm the validity of the proposed method both for batch scenarios and online scenarios where document collections can grow over time.

A neural model for fuzzy Dempster–Shafer classifiers

Abstract This paper presents a supervised classification model integrating fuzzy reasoning and Dempster–Shafer propagation of evidence has been built on top of connectionist techniques to address classification tasks in which vagueness and ambiguity coexist. The salient aspect of the approach is the integration within a neuro-fuzzy system of knowledge structures and inferences for evidential reasoning based on Dempster–Shafer theory. In this context the learning task can be formulated as the search for the most adequate “ingredients” of the fuzzy and Dempster–Shafer frameworks such as the fuzzy aggregation operators, for fusing data from different sources and focal elements, and basic probability assignments, describing the contributions of evidence in the inference scheme. The new neural model allows us to establish a complete correspondence between connectionist elements and fuzzy and Dempster–Shafer ingredients, ensuring both a high level of interpretability, and transparency and high performance in classification. Experiments with simulated data show that the network can cope well with problems of different complexity. The experiments with real data show the superiority of the neural implementation with respect to the symbolic representation, and prove that the integration of the propagation of evidence provides better classification results and fuzzy reasoning within connectionist schema than those obtained by pure neuro-fuzzy models.

Text Localization Based on Fast Feature Pyramids and Multi-Resolution Maximally Stable Extremal Regions

Text localization from scene images is a challenging task that finds application in many areas. In this work, we propose a novel hybrid text localization approach that exploits Multi-resolution Maximally Stable Extremal Regions to discard false-positive detections from the text confidence maps generated by a Fast Feature Pyramid based sliding window classifier. The use of a multi-scale approach during both feature computation and connected component extraction allows our method to identify uncommon text elements that are usually not detected by competing algorithms, while the adoption of approximated features and appropriately filtered connected components assures a low overall computational complexity of the proposed system.

Neural adaptive stereo matching

The present work investigates the potential of neural adaptive learning to solve the correspondence problem within a two-frame adaptive area matching approach. A novel method is proposed based on the use of the zero mean normalized cross-correlation coefficient integrated within a neural network model which uses a least-mean-square delta rule for training.Two experiments were conducted for evaluating the neural model proposed. The first aimed to produce dense disparity maps based on the analysis of standard test images. The second experiment, conducted in the biomedical field, aimed to model 3D surfaces from a varied set of scanning electron microscope stereoscopic image pairs.

Robust Angle Invariant 1D Barcode Detection

Barcode reading mobile applications that identify products from pictures taken using mobile devices are widely used by customers to perform online price comparisons or to access reviews written by others. Most of the currently available barcode reading approaches focus on decoding degraded barcodes and treat the underlying barcode detection task as a side problem that can be addressed using appropriate object detection methods. However, the majority of modern mobile devices do not meet the minimum working requirements of complex general purpose object detection algorithms and most of the efficient specifically designed barcode detection algorithms require user interaction to work properly. In this paper, we present a novel method for barcode detection in camera captured images based on a supervised machine learning algorithm that identifies one-dimensional barcodes in the two-dimensional Hough Transform space. Our model is angle invariant, requires no user interaction and can be executed on a modern mobile device. It achieves excellent results for two standard one-dimensional barcode datasets: WWU Muenster Barcode Database and ArTe-Lab 1D Medium Barcode Dataset. Moreover, we prove that it is possible to enhance the overall performance of a state-of-the-art barcode reading algorithm by combining it with our detection method.

Clustering of short commercial documents for the web

Document clustering techniques have been applied in several areas, with the Web as one of the most recent and influent. Both general-purpose and text-oriented techniques exist and can be used to cluster a collection of documents in many ways. In this work we propose an online, single-pass document clustering model that can be combined with a variety of text-oriented similarity measures. An experimental evaluation of the proposed model was conducted in the e-commerce domain. Performances were measured using a clustering-oriented metric based on F-Measure and compared with those obtained by other well-known approaches.

A mobile visual search application for content based image retrieval in the fashion domain

In this study we propose a mobile application which interfaces with a Content-Based Image Retrieval engine for online shopping in the fashion domain. Using this application it is possible to take a picture of a garment to retrieve its most similar products. The proposed method is firstly presented as an application in which the user manually select the name of the subject framed by the camera, before sending the request to the server. In the second part we propose an advanced approach which automatically classifies the object of interest, in this way it is possible to minimize the effort required by the user during the query process. In order to evaluate the performance of the proposed method, we have collected three datasets: the first contains clothing images of products taken from different online shops, whereas for the other datasets we have used images and video frames of clothes taken by Internet users. The results show the feasibility in the use of the proposed mobile application in a real scenario.

Dense Two-Frame Stereo Correspondence by Self-organizing Neural Network

This work aims at defining an extension of a competitive method for matching correspondences in stereoscopic image analysis. The method we extended was proposed by Venkatesh, Y.V. et al where the authors extend a Self-Organizing Map by changing the neural weights updating phase in order to solve the correspondence problem within a two-frame area matching approach and producing dense disparity maps. In the present paper we have extended the method mentioned by adding some details that lead to better results. Experimental studies were conducted to evaluate and compare the solution proposed.

Neural disparity computation for dense two-frame stereo correspondence

This work aims at defining a new method for matching correspondences in stereoscopic image analysis. A representation of occlusions drives the overall matching process. Based on the taxonomy proposed by Scharstein and Szelinsky (2002, IJCV, 47, 7-42), the dense stereo matching process is divided into three tasks: matching cost computation, aggregation of local evidence and computation of disparity values. Within the second and third phases new strategies are introduced in an attempt to improve the reliability of results. Aggregation is based on a new local matching measure, and neural techniques compute disparities adaptively. Two experimental studies were conducted to evaluate and compare the solutions proposed. The first uses a standard well-known dataset including data with true disparity maps; the second study was conducted on complex real images acquired by a scanning electron microscope (SEM).