Publications by Rita Cucchiara

In this paper we evaluate the performance of scene detection techniques, starting from the classic precision/recall approach, moving to the better designed coverage/overflow measures, and finally proposing an improved metric, in order to solve frequently observed cases in which the numeric interpretation is different from the expected results. Numerical evaluation is performed on two recent proposals for automatic scene detection, and comparing them with a simple but effective novel approach. Experimental results are conducted to show how different measures may lead to different interpretations.

Recent egocentric video summarization approaches have dealt with motion analysis and social interaction without considering that user can be interested in preserving only part of the video related to his interests. In this paper we propose a new method for personalized video summarization of cultural experiences with the goal of extracting from the streams only the scenes corresponding to a user's specific topics request, chosen among the shots in which it's possible to deduce that the visitor was focusing on a point of interest. Preliminary experiments show that our approach is promising and allows visitor to better customize the summary of his experience.

Scene detection is a fundamental tool for allowing effective video browsing and re-using. In this paper we present a model that automatically divides videos into coherent scenes, which is based on a novel combination of local image descriptors and temporal clustering techniques. Experiments are performed to demonstrate the effectiveness of our approach, by comparing our algorithm against two recent proposals for automatic scene segmentation. We also propose improved performance measures that aim to reduce the gap between numerical evaluation and expected results.

Video decomposition techniques are fundamental tools for allowing effective video browsing and re-using. In this work, we consider the problem of segmenting broadcast videos into coherent scenes, and propose a scene detection algorithm based on hierarchical clustering, along with a very fast state-of-the-art shot segmentation approach. Experiments are performed to demonstrate the effectiveness of our algorithms, by comparing against recent proposals for automatic shot and scene segmentation.

Conventional experiments on MTT are built upon the belief that fixing the detections to different trackers is sufficient to obtain a fair comparison. In this work we argue how the true behavior of a tracker is exposed when evaluated by varying the input detections rather than by fixing them. We propose a systematic and reproducible protocol and a MATLAB toolbox for generating synthetic data starting from ground truth detections, a proper set of metrics to understand and compare trackers peculiarities and respective visualization solutions.

The understanding of mutual people interaction is a key component for recognizing people social behavior, but it strongly relies on a personal point of view resulting difficult to be a-priori modeled. We propose the adoption of the unique head mounted cameras first person perspective (ego-vision) to promptly detect people interaction in different social contexts. The proposal relies on a complete and reliable system that extracts people׳s head pose combining landmarks and shape descriptors in a temporal smoothed HMM framework. Finally, interactions are detected through supervised clustering on mutual head orientation and people distances exploiting a structural learning framework that specifically adjusts the clustering measure according to a peculiar scenario. Our solution provides the flexibility to capture the interactions disregarding the number of individuals involved and their level of acquaintance in context with a variable degree of social involvement. The proposed system shows competitive performances on both publicly available ego-vision datasets and ad hoc benchmarks built with real life situations.

The interest in cultural cities is in constant growth, and so is the demand for new multimedia tools and applications that enrich their fruition. In this paper we propose an egocentric vision system to enhance tourists' cultural heritage experience. Exploiting a wearable board and a glass-mounted camera, the visitor can retrieve architectural details of the historical building he is observing and receive related multimedia contents. To obtain an effective retrieval procedure we propose a visual descriptor based on the covariance of local features. Differently than the common Bag of Words approaches our feature vector does not rely on a generated visual vocabulary, removing the dependence from a specific dataset and obtaining a reduction of the computational cost. 3D modeling is used to achieve a precise visitor's localization that allows browsing visible relevant details that the user may otherwise miss. Experimental results conducted on a publicly available cultural heritage dataset show that the proposed feature descriptor outperforms Bag of Words techniques.

Three-dimensional object recognition on range data and 3D point clouds is becoming more important nowadays. Since many real objects have a shape that could be approximated by simple primitives, robust pattern recognition can be used to search for primitive models. For example, the Hough transform is a well-known technique which is largely adopted in 2D image space. In this paper, we systematically analyze different probabilistic/randomized Hough transform algorithms for spherical object detection in dense point clouds. In particular, we study and compare four variants which are characterized by the number of points drawn together for surface computation into the parametric space and we formally discuss their models. We also propose a new method that combines the advantages of both single-point and multi-point approaches for a faster and more accurate detection. The methods are tested on synthetic and real datasets.

In this paper, we propose a general approach for automatic segmentation, color-based retrieval and classification of garments in fashion store databases, exploiting shape and color information. The garment segmentation is automatically initialized by learning geometric constraints and shape cues, then it is performed by modeling both skin and accessory colors with Gaussian Mixture Models. For color similarity retrieval and classification, to adapt the color description to the users’ perception and the company marketing directives, a color histogram with an optimized binning strategy, learned on the given color classes, is introduced and combined with HOG features for garment classification. Experiments validating the proposed strategy, and a free-to-use dataset publicly available for scientific purposes, are finally detailed.

Several papers addressed ellipse detection as a first step for several computer vision applications, but most of the proposed solutions are too slow to be applied in real time on large images or with limited hardware resources. This paper presents a novel algorithm for fast and effective ellipse detection and demonstrates its superior speed performance on large and challenging datasets. The proposed algorithm relies on an innovative selection strategy of arcs which are candidate to form ellipses and on the use of Hough transform to estimate parameters in a decomposed space. The final aim of this solution is to represent a building block for new generation of smart-phone applications which need fast and accurate ellipse detection also with limited computational resources. © 2014 Elsevier Ltd.