Raimondo Schettini

Quantitative evaluation of color image segmentation results

In this paper we consider the problem of the automatic evaluation of the results of color image segmentation. Liu and Yang (1994) have proposed an evaluation function, inspired by the qualitative criteria for good image segmentation established by Haralick and Shapiro (1985), that does not require that the user set any parameter or threshold value. We identify some limitations in this evaluation function, and propose two enhanced functions that correspond more closely to visual judgment.

Image annotation using SVM

The paper describes an innovative image annotation tool for classifying image regions in one of seven classes - sky, skin, vegetation, snow, water, ground, and buildings - or as unknown. This tool could be productively applied in the management of large image and video databases where a considerable volume of images/frames there must be automatically indexed. The annotation is performed by a classification system based on a multi-class Support Vector Machine. Experimental results on a test set of 200 images are reported and discussed.

Underwater image processing: state of the art of restoration and image enhancement methods

The underwater image processing area has received considerable attention within the last decades, showing important achievements. In this paper we review some of the most recent methods that have been specifically developed for the underwater environment. These techniques are capable of extending the range of underwater imaging, improving image contrast and resolution. After considering the basic physics of the light propagation in the water medium, we focus on the different algorithms available in the literature. The conditions for which each of them have been originally developed are highlighted as well as the quality assessment methods used to evaluate their performance.The underwater image processing area has received considerable attention within the last decades, showing important achievements. In this paper we review some of the most recent methods that have b...

3D face detection using curvature analysis

Face detection is a crucial preliminary in many applications. Most of the approaches to face detection have focused on the use of two-dimensional images. We present an innovative method that combines a feature-based approach with a holistic one for three-dimensional (3D) face detection. Salient face features, such as the eyes and nose, are detected through an analysis of the curvature of the surface. Each triplet consisting of a candidate nose and two candidate eyes is processed by a PCA-based classifier trained to discriminate between faces and non-faces. The method has been tested, with good results, on some 150 3D faces acquired by a laser range scanner.

An innovative algorithm for key frame extraction in video summarization

Video summarization, aimed at reducing the amount of data that must be examined in order to retrieve the information desired from information in a video, is an essential task in video analysis and indexing applications. We propose an innovative approach for the selection of representative (key) frames of a video sequence for video summarization. By analyzing the differences between two consecutive frames of a video sequence, the algorithm determines the complexity of the sequence in terms of changes in the visual content expressed by different frame descriptors. The algorithm, which escapes the complexity of existing methods based, for example, on clustering or optimization strategies, dynamically and rapidly selects a variable number of key frames within each sequence. The key frames are extracted by detecting curvature points within the curve of the cumulative frame differences. Another advantage is that it can extract the key frames on the fly: curvature points can be determined while computing the frame differences and the key frames can be extracted as soon as a second high curvature point has been detected. We compare the performance of this algorithm with that of other key frame extraction algorithms based on different approaches. The summaries obtained have been objectively evaluated by three quality measures: the Fidelity measure, the Shot Reconstruction Degree measure and the Compression Ratio measure.

Color-based image retrieval using spatial-chromatic histograms

Abstract The paper describes a new indexing methodology for image databases integrating color and spatial information for content-based image retrieval. This methodology, called Spatial-Chromatic Histogram (SCH), synthesizing in few values information about the location of pixels having the same color and their arrangement within the image, can be more satisfactory than standard techniques when the user would like to retrieve from the database the images that actually resemble the query image selected in their color distribution characteristics. Experimental trials on a database of about 3000 images are reported and compared with more standard techniques, like Color Coherence Vectors, on the basis of human perceptual judgments.

A relevance feedback mechanism for content-based image retrieval

Abstract Content-based image retrieval systems require the development of relevance feedback mechanisms that allow the user to progressively refine the systems response to a query. In this paper a new relevance feedback mechanism is described which evaluates the feature distributions of the images judged relevant, or not relevant, by the user and dynamically updates both the similarity measure and the query in order to accurately represent the users particular information needs. Experimental results demonstrate the effectiveness of this mechanism.

A segmentation algorithm for color images

Abstract A method that combines clustering and region merging for color image segmentation is presented. We start with clustering based on recursive one-dimensional histogram analysis, where the parameter that controls the segmentation process has been set to produce an oversegmentation. The resulting regions are then merged on the basis of a criterion that takes into account color similarity and spatial proximity.

Color balancing of digital photos using simple image statistics

The great diffusion of digital cameras and the widespread use of the internet have produced a mass of digital images depicting a huge variety of subjects, generally acquired by unknown imaging systems under unknown lighting conditions. This makes color balancing, recovery of the color characteristics of the original scene, increasingly difficult. In this paper, we describe a method for detecting and removing a color cast (i.e. a superimposed color due to lighting conditions, or to the characteristics of the capturing device), from a digital photo without any a priori knowledge of its semantic content. First a cast detector, using simple image statistics, classifies the input images as presenting no cast, evident cast, ambiguous cast, a predominant color that must be preserved (such as in underwater images or single color close-ups) or as unclassifiable. A cast remover, a modified version of the white balance algorithm, is then applied in cases of evident or ambiguous cast. The method we propose has been tested with positive results on a data set of some 750 photos.

Image registration by recognition of corresponding structures

A method for automatic image registration which is characterized by its insensitivity to scaling, rotation, and intensity changes is described. The method is based on similarity assessment of the structures in the images and on a check of their spatial arrangement. Pairs of structures that correspond to each other provide sets of control points to geometric mapping functions. An application of the method to remote-sensing image alignment with a reference map is presented. >