Artistic ideation based on computer vision methods
Ferrán Reverter, Pilar Rosado Rodrigo, Eva Figueras Ferrer, Miquel Àngel Planas · Applied Computer Science · 2012
This paper analyzes the automatic classification of scenes that are the basis of the ideation and the designing of the sculptural production of an artist. The main purpose is to evaluate the per- formance of the Bag-of-Features methods, in the challenging task of categorizing scenes when scenes differ in semantics rather than the objects they contain. We have employed a kernel-based recognition method that works by computing rough geometric correspondence on a global scale using the pyramid matching scheme introduced by Lazebnik (7). Results are promising, on average the score is about 70%. Experiments suggest that the automatic categorization of images based on computer vision methods can provide objective principles in cataloging images. Image representation is a very important element for image classification, annotation, seg- mentation or retrieval. Nearly all the methods in computer vision which deals with image content representation resort to features capable of representing image content in a compact way. Local features based representation can produce a versatile and robust image representa- tion capable of representing global and local content at the same time. Describing an object or scene using local features computed at interest locations makes the description robust to par- tial occlusion and image transformation. This results from the local character of the features and their invariance to image transformations. The bag-of-visterms (BOV) is an image representation built from automatically extracted and quantized local descriptors referred to as visterms in the remainder of this paper. The BOV representation, which is derived from these local features, has been shown to be one of the best image representations in several tasks. The main objective of this study is assessing the performance of SIFT descriptors, BOV representation and spatial pyramid matching for automatic analysis of images that are the basis of the ideation and designing of art work. Additionally, we explore the capability of this kind of modelization to become useful for the production of software based art.