A. Del Bimbo

Visual image retrieval by elastic matching of user sketches

Effective image retrieval by content from database requires that visual image properties are used instead of textual labels to properly index and recover pictorial data. Retrieval by shape similarity, given a user-sketched template is particularly challenging, owing to the difficulty to derive a similarity measure that closely conforms to the common perception of similarity by humans. In this paper, we present a technique which is based on elastic matching of sketched templates over the shapes in the images to evaluate similarity ranks. The degree of matching achieved and the elastic deformation energy spent by the sketch to achieve such a match are used to derive a measure of similarity between the sketch and the images in the database and to rank images to be displayed. The elastic matching is integrated with arrangements to provide scale invariance and take into account spatial relationships between objects in multi-object queries. Examples from a prototype system are expounded with considerations about the effectiveness of the approach and comparative performance analysis.

A SIFT-Based Forensic Method for Copy–Move Attack Detection and Transformation Recovery

One of the principal problems in image forensics is determining if a particular image is authentic or not. This can be a crucial task when images are used as basic evidence to influence judgment like, for example, in a court of law. To carry out such forensic analysis, various technological instruments have been developed in the literature. In this paper, the problem of detecting if an image has been forged is investigated; in particular, attention has been paid to the case in which an area of an image is copied and then pasted onto another zone to create a duplication or to cancel something that was awkward. Generally, to adapt the image patch to the new context a geometric transformation is needed. To detect such modifications, a novel methodology based on scale invariant features transform (SIFT) is proposed. Such a method allows us to both understand if a copy-move attack has occurred and, furthermore, to recover the geometric transformation used to perform cloning. Extensive experimental results are presented to confirm that the technique is able to precisely individuate the altered area and, in addition, to estimate the geometric transformation parameters with high reliability. The method also deals with multiple cloning.

Semantics in visual information retrieval

A compositional approach increases the level of representation that can be automatically extracted and used in a visual information retrieval system. Visual information at the perceptual level is aggregated according to a set of rules. These rules reflect the specific context and transform perceptual words into phrases capturing pictorial content at a higher, and closer to the human, semantic level.

Retrieval by shape similarity with perceptual distance and effective indexing

An important problem in accessing and retrieving visual information is to provide efficient similarity matching in large databases. Though much work is being done on the investigation of suitable perceptual models and the automatic extraction of features, little attention is given to the combination of useful representations and similarity models with efficient index structures. In this paper we propose retrieval by shape similarity using local descriptors and effective indexing. Shapes are partitioned into tokens in correspondence with their protrusions, and each token is modeled according to a set of perceptually salient attributes. Shape indexing is obtained by arranging shape tokens into a suitably modified M-tree index structure. Two distinct distance functions model respectively, token and shape perceptual similarity. Examples from a prototype system and computational experiences are reported for both retrieval accuracy and indexing efficiency. Shape retrieval has been tested under shape scaling, orientation changes, and partial shape occlusions. A comparative analysis of different indexing structures, for shape retrieval is presented.

Efficient matching and indexing of graph models in content-based retrieval

In retrieval from image databases, evaluation of similarity, based both on the appearance of spatial entities and on their mutual relationships, depends on content representation based on attributed relational graphs. This kind of modeling entails complex matching and indexing, which presently prevents its usage within comprehensive applications. In this paper, we provide a graph-theoretical formulation for the problem of retrieval based on the joint similarity of individual entities and of their mutual relationships and we expound its implications on indexing and matching. In particular, we propose the usage of metric indexing to organize large archives of graph models, and we propose an original look-ahead method which represents an efficient solution for the (sub)graph error correcting isomorphism problem needed to compute object distances. Analytic comparison and experimental results show that the proposed lookahead improves the state-of-the-art in state-space search methods and that the combined use of the proposed matching and indexing scheme permits for the management of the complexity of a typical application of retrieval by spatial arrangement.

3D Face Recognition Using Isogeodesic Stripes

In this paper, we present a novel approach to 3D face matching that shows high effectiveness in distinguishing facial differences between distinct individuals from differences induced by nonneutral expressions within the same individual. The approach takes into account geometrical information of the 3D face and encodes the relevant information into a compact representation in the form of a graph. Nodes of the graph represent equal width isogeodesic facial stripes. Arcs between pairs of nodes are labeled with descriptors, referred to as 3D Weighted Walkthroughs (3DWWs), that capture the mutual relative spatial displacement between all the pairs of points of the corresponding stripes. Face partitioning into isogeodesic stripes and 3DWWs together provide an approximate representation of local morphology of faces that exhibits smooth variations for changes induced by facial expressions. The graph-based representation permits very efficient matching for face recognition and is also suited to being employed for face identification in very large data sets with the support of appropriate index structures. The method obtained the best ranking at the SHREC 2008 contest for 3D face recognition. We present an extensive comparative evaluation of the performance with the FRGC v2.0 data set and the SHREC08 data set.

Soccer highlights detection and recognition using HMMs

In this paper we report on our experience in the detection and recognition of soccer highlights in videos using hidden Markov models. A first approach relies on camera motion only, whereas a second one also includes information regarding the location of players on the playing field. While the former approach requires less information, the latter has proven to be more precise. Our experimental evaluation yields interesting results.

Metric 3D reconstruction and texture acquisition of surfaces of revolution from a single uncalibrated view

Image analysis and computer vision can be effectively employed to recover the three-dimensional structure of imaged objects, together with their surface properties. In this paper, we address the problem of metric reconstruction and texture acquisition from a single uncalibrated view of a surface of revolution (SOR). Geometric constraints induced in the image by the symmetry properties of the SOR structure are exploited to perform self-calibration of a natural camera, 3D metric reconstruction, and texture acquisition. By exploiting the analogy with the geometry of single axis motion, we demonstrate that the imaged apparent contour and the visible segments of two imaged cross sections in a single SOR view provide enough information for these tasks. Original contributions of the paper are: single view self-calibration and reconstruction based on planar rectification, previously developed for planar surfaces, has been extended to deal also with the SOR class of curved surfaces; self-calibration is obtained by estimating both camera focal length (one parameter) and principal point (two parameters) from three independent linear constraints for the SOR fixed entities; the invariant-based description of the SOR scaling function has been extended from affine to perspective projection. The solution proposed exploits both the geometric and topological properties of the transformation that relates the apparent contour to the SOR scaling function. Therefore, with this method, a metric localization of the SOR occluded parts can be made, so as to cope with them correctly. For the reconstruction of textured SORs, texture acquisition is performed without requiring the estimation of external camera calibration parameters, but only using internal camera parameters obtained from self-calibration.

VISUAL QUERYING BY COLOR PERCEPTIVE REGIONS

A major research subject in image databases is to support efficient and effective access to images based on their visual content. In color image databases, this requires to support image retrieval by both global and local chromatic features. Image retrieval by color regions is complicated by the fact that regions which are requested in the query should correspond to relevant colored regions in the retrieved images. In the following we present the PICASSO system, which supports image indexing and retrieval based on colors. The system exploits a pyramidal representation of images. Multiple descriptions of image properties are created at different levels of resolution thus allowing effective retrieval through specific queries as well as imprecise ones.

Image retrieval by color semantics

Abstract. The development of a system supporting querying of image databases by color content tackles a major design choice about properties of colors which are referenced within user queries. On the one hand, low-level properties directly reflect numerical features and concepts tied to the machine representation of color information. On the other hand, high-level properties address concepts such as the perceptual quality of colors and the sensations that they convey. Color-induced sensations include warmth, accordance or contrast, harmony, excitement, depression, anguish, etc. In other words, they refer to the semantics of color usage. In particular, paintings are an example where the message is contained more in the high-level color qualities and spatial arrangements than in the physical properties of colors. Starting from this observation, Johannes Itten introduced a formalism to analyze the use of color in art and the effects that this induces on the users psyche. In this paper, we present a system which translates the Itten theory into a formal language that expresses the semantics associated with the combination of chromatic properties of color images. The system exploits a competitive learning technique to segment images into regions with homogeneous colors. Fuzzy sets are used to represent low-level region properties such as hue, saturation, luminance, warmth, size and position. A formal language and a set of model-checking rules are implemented to define semantic clauses and verify the degree of truth by which they hold over an image.