Machine learning for image classification and clustering using a universal distance measure

Uzi Chester, Joel Ratsaby

פרסום מחקרי: פרק בספר / בדוח / בכנספרסום בספר כנסביקורת עמיתים

11 ציטוטים ‏(Scopus)

תקציר

We present a new method for image feature-extraction which is based on representing an image by a finite-dimensional vector of distances that measure how different the image is from a set of image prototypes. We use the recently introduced Universal Image Distance (UID) [1] to compare the similarity between an image and a prototype image. The advantage in using the UID is the fact that no domain knowledge nor any image analysis need to be done. Each image is represented by a finite dimensional feature vector whose components are the UID values between the image and a finite set of image prototypes from each of the feature categories. The method is automatic since once the user selects the prototype images, the feature vectors are automatically calculated without the need to do any image analysis. The prototype images can be of different size, in particular, different than the image size. Based on a collection of such cases any supervised or unsupervised learning algorithm can be used to train and produce an image classifier or image cluster analysis. In this paper we present the image feature-extraction method and use it on several supervised and unsupervised learning experiments for satellite image data. The feature-extraction method is scalable and is easily implementable on multi-core computing resources.

שפה מקוריתאנגלית
כותר פרסום המארחSimilarity Search and Applications - 6th International Conference, SISAP 2013, Proceedings
עמודים59-72
מספר עמודים14
מזהי עצם דיגיטלי (DOIs)
סטטוס פרסוםפורסם - 2013
אירוע6th International Conference on Similarity Search and Applications, SISAP 2013 - A Coruna, ספרד
משך הזמן: 2 אוק׳ 20134 אוק׳ 2013

סדרות פרסומים

שםLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
כרך8199 LNCS
ISSN (מודפס)0302-9743
ISSN (אלקטרוני)1611-3349

כנס

כנס6th International Conference on Similarity Search and Applications, SISAP 2013
מדינה/אזורספרד
עירA Coruna
תקופה2/10/134/10/13

טביעת אצבע

להלן מוצגים תחומי המחקר של הפרסום 'Machine learning for image classification and clustering using a universal distance measure'. יחד הם יוצרים טביעת אצבע ייחודית.

פורמט ציטוט ביבליוגרפי