Jorma Laaksonen

The 2005 PASCAL visual object classes challenge

The PASCAL Visual Object Classes Challenge ran from February to March 2005. The goal of the challenge was to recognize objects from a number of visual object classes in realistic scenes (i.e. not pre-segmented objects). Four object classes were selected: motorbikes, bicycles, cars and people. Twelve teams entered the challenge. In this chapter we provide details of the datasets, algorithms used by the teams, evaluation criteria, and results achieved.

PicSOM—content-based image retrieval with self-organizing maps

We have developed a novel system for content-based image retrieval in large, unannotated databases. The system is called PicSOM, and it is based on tree structured self-organizing maps (TS-SOMs). Given a set of reference images, PicSOM is able to retrieve another set of images which are similar to the given ones. Each TS-SOM is formed with a diAerent image feature representation like color, texture, or shape. A new technique introduced in PicSOM facilitates automatic combination of responses from multiple TS-SOMs and their hierarchical levels. This mechanism adapts to the user’s preferences in selecting which images resemble each other. Thus, the mechanism implements a relevance feedback technique on content-based image retrieval. The image queries are performed through the World Wide Web and the queries are iteratively refined as the system exposes more images to the user. ” 2000 Elsevier Science B.V. All rights reserved.

Variants of self-organizing maps

Self-organizing maps have a connection with traditional vector quantization. A characteristic which makes them resemble certain biological brain maps, however, is the spatial order of their responses which is formed in the learning process. Two innovations are discussed: dynamic weighting of the input signals at each input of each cell, which improves the ordering when very different input signals are used, and definition of neighborhoods in the learning algorithm by the minimum spanning tree, which provides a far better and faster approximation of prominently structured density functions. It is cautioned that if the maps are used for pattern recognition and decision processes, it is necessary to fine-tune the reference vectors such that they directly define the decision borders.<<ETX>>

LVQPAK: A software package for the correct application of Learning Vector Quantization algorithms

An overview of the software package LVQPAK, which has been developed for convenient and effective application of learning vector quantization algorithms, is presented. Two new features are included: fast conflict-free initial distribution of codebook vectors into the class zones and the optimized-learning-rate algorithm OLVQ1.<<ETX>>

Neural and statistical classifiers-taxonomy and two case studies

Pattern classification using neural networks and statistical methods is discussed. We give a tutorial overview in which popular classifiers are grouped into distinct categories according to their underlying mathematical principles; also, we assess what makes a classifier neural. The overview is complemented by two case studies using handwritten digit and phoneme data that test the performance of a number of most typical neural-network and statistical classifiers. Four methods of our own are included: reduced kernel discriminant analysis, the learning k-nearest neighbors classifier, the averaged learning subspace method, and a version of kernel discriminant analysis.

Using diversity of errors for selecting members of a committee classifier

Diversity of classifiers is generally accepted as being necessary for combining them in a committee. Quantifying diversity of classifiers, however, is difficult as there is no formal definition thereof. Numerous measures have been proposed in literature, but their performance is often know to be suboptimal. Here several common methods are compared with a novel approach focusing on the diversity of the errors made by the member classifiers. Experiments with combining classifiers for handwritten character recognition are presented. The results show that the approach of diversity of errors is beneficial, and that the novel exponential error count measure is capable of consistently finding an effective member classifier set.

Statistical shape features in content-based image retrieval

In this article the use of shape features in content-based image retrieval is studied. The emphasis is on techniques which do not demand object segmentation. PicSOM, the image retrieval system used in the experiments, requires that features are represented by constant-sized feature vectors for which the Euclidean distance can be used as a similarity measure. The shape features suggested here are edge histograms and Fourier transform based features computed for an edge image in Cartesian and polar coordinate planes. The results show that both local and global shape features are important clues of shapes in an image.

PicSOM: self-organizing maps for content-based image retrieval

Content-based image retrieval is an important approach to the problem of processing the increasing amount of visual data. It is based on automatically extracted features from the content of the images, such as color, texture, shape and structure. We have started a project to study methods for content-based image retrieval using the self-organizing map (SOM) as the image similarity scoring method. Our image retrieval system, named PicSOM, can be seen as a SOM-based approach to relevance feedback which is a form of supervised learning to adjust the subsequent queries based on the users responses during the information retrieval session. In PicSOM, a separate tree structured SOM (TS-SOM) is trained for each feature vector type in use. The system then adapts to the users preferences by returning her more images from those SOMs where her responses have been most densely mapped.

Self-Organising Maps as a relevance feedback technique in Content-Based image retrieval

Abstract:Self-Organising Maps (SOMs) can be used in implementing a powerful relevance feedback mechanism for Content-Based Image Retrieval (CBIR). This paper introduces the PicSOM CBIR system, and describes the use of SOMs as a relevance feedback technique in it. The technique is based on the SOM’s inherent property of topology-preserving mapping from a high-dimensional feature space to a two-dimensional grid of artificial neurons. On this grid similar images are mapped in nearby locations. As image similarity must, in unannotated databases, be based on low-level visual features, the similarity of images is dependent on the feature extraction scheme used. Therefore, in PicSOM there exists a separate tree-structured SOM for each different feature type. The incorporation of the relevance feedback and the combination of the outputs from the SOMs are performed as two successive processing steps. The proposed relevance feedback technique is described, analysed qualitatively, and visualised in the paper. Also, its performance is compared with a reference method.

Detecting Man-Made Structures and Changes in Satellite Imagery With a Content-Based Information Retrieval System Built on Self-Organizing Maps

The increasing amount and resolution of satellite sensors demand new techniques for browsing remote sensing image archives. Content-based querying allows an efficient retrieval of images based on the information they contain, rather than their acquisition date or geographical extent. Self-organizing maps (SOMs) have been successfully applied in the PicSOM system to content-based image retrieval in databases of conventional images. In this paper, we investigate and extend the potential of PicSOM for the analysis of remote sensing data. We propose methods for detecting man-made structures, as well as supervised and unsupervised change detection, based on the same framework. In this paper, a database was artificially created by splitting each satellite image to be analyzed into small images. After training the PicSOM on this imagelet database, both interactive and off-line queries were made to detect man-made structures, as well as changes between two very high resolution images from different years. Experimental results were both evaluated quantitatively and discussed qualitatively, and suggest that this new approach is suitable for analyzing very high resolution optical satellite imagery. Possible applications of this work include interactive detection of man-made structures or supervised monitoring of sensitive sites