We have approached the difficulties of automatic cataloguing of images on which the conception and design of sculptor M. Planas artistic production are based. In order to build up a visual vocabulary for basing image description on, we followed a procedure similar to the method Bag-of-Words (BOW). We have implemented a probabilistic latent semantic analysis (PLSA) that detects underlying topics in images. Whole image collection was clustered into different types that describe aesthetic preferences of the artist. The outcomes are promising, the described cataloguing method may provide new viewpoints for the artist in future works.

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Semantic-Based Image Analysis with the Goal of Assisting Artistic Creation

This study uses computer vision models, which to some extent simulate the initial stages of human visual perception, to help categorize data in large sets of images of artworks by the artist Antoni...

Computer Vision Models to Categorize Art Collections According to the Visual Content: A New Approach to the Abstract Art of Antoni Tàpies

This paper analyzes the automatic classification of scenes that are the basis of the ideation and the designing of the sculptural production of the sculptor Miquel Planas. The main purpose is to evaluate the performance 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, Schmid and Ponce in 2006. 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 .

La visión artificial, un nuevo aliado para el análisis de imágenes artísticas Computer vision, a new ally for the analysis of artistic images

The objective of our research is to develop a series of computer vision programs to search for analogies in large datasets—in this case, collections of images of abstract paintings—based solely on their visual content without textual annotation. We have programmed an algorithm based on a specific model of image description used in computer vision. This approach involves placing a regular grid over the image and selecting a pixel region around each node. Dense features computed over this regular grid with overlapping patches are used to represent the images. Analysing the distances between the whole set of image descriptors we are able to group them according to their similarity and each resulting group will determines what we call "visual words". This model is called Bag-of-Words representation Given the frequency with which each visual word occurs in each image, we apply the method pLSA (Probabilistic Latent Semantic Analysis), a statistical model that classifies fully automatically, without any textual annotation, images according to their formal patterns. In this way, the researchers hope to develop a tool both for producing and analysing works of art.

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Del píxel a las resonancias visuales: la imagen con voz propia

The General Shape Analysis (GSA) is a task similar to the shape recognition and retrieval. However, in GSA an object usually does not belong to a template class, but can only be similar to some of them. Moreover, the number of applied templates is limited. Usually, ten most general shapes are used. Hence, the GSA consists in searching for the most universal information about them. This is useful when some general information has to be concluded, e.g. in coarse classification. In this paper the result of the application of three shape descriptors based on the moment theory to the GSA is presented. For this purpose the Moment Invariants, Contour Sequence Moments, and Zernike Moments were selected.

Application of the Moment Shape Representations to the General Shape Analysis

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.

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Miquel Àngel Planas

Papers

Artistic ideation based on computer vision methods

Semantic-Based Image Analysis with the Goal of Assisting Artistic Creation