Journal Article•
Artistic ideation based on computer vision methods
01 Jan 2012-Applied Computer Science (Computer Science Commission of Polish Academy of Science)-Vol. 6, Iss: 2, pp 72-78
TL;DR: 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 explores the capability of this kind of modelization to become useful for the production of software based art.
Abstract: 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.
Citations
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15 Sep 2014
TL;DR: A probabilistic latent semantic analysis (PLSA) that detects underlying topics in images and builds up a visual vocabulary for basing image description on is implemented.
Abstract: 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.
3 citations
Cites result from "Artistic ideation based on computer..."
...This study extends previous work in assessing the performance of SIFT descriptors, BOW representation and spatial pyramid matching for automatic analysis of images that are the basis of the ideation and designing of art work [3]....
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TL;DR: 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 Tàpies, to establish analogies between different artists or periods using the same criteria.
Abstract: 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...
3 citations
01 Jan 2016
TL;DR: In this article, the authors evaluated 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.
Abstract: 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 .
1 citations
TL;DR: The result of the application of three shape descriptors based on the moment theory to the General Shape Analysis is presented and the Moment Invariants, Contour Sequence Moments, and Zernike Moments were selected.
Abstract: 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.
Cites background from "Artistic ideation based on computer..."
...Given the rapid development of computer vision applications, where the image patterns analysis (ranging from medical diagnosis [1, 10], to analysing materials [4], to artistic applications [11]) requires a significant computation time, any procedure for preclassification can be very valuable....
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References
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TL;DR: This paper presents a method for extracting distinctive invariant features from images that can be used to perform reliable matching between different views of an object or scene and can robustly identify objects among clutter and occlusion while achieving near real-time performance.
Abstract: This paper presents a method for extracting distinctive invariant features from images that can be used to perform reliable matching between different views of an object or scene. The features are invariant to image scale and rotation, and are shown to provide robust matching across a substantial range of affine distortion, change in 3D viewpoint, addition of noise, and change in illumination. The features are highly distinctive, in the sense that a single feature can be correctly matched with high probability against a large database of features from many images. This paper also describes an approach to using these features for object recognition. The recognition proceeds by matching individual features to a database of features from known objects using a fast nearest-neighbor algorithm, followed by a Hough transform to identify clusters belonging to a single object, and finally performing verification through least-squares solution for consistent pose parameters. This approach to recognition can robustly identify objects among clutter and occlusion while achieving near real-time performance.
46,906 citations
17 Jun 2006
TL;DR: This paper presents a method for recognizing scene categories based on approximate global geometric correspondence that exceeds the state of the art on the Caltech-101 database and achieves high accuracy on a large database of fifteen natural scene categories.
Abstract: This paper presents a method for recognizing scene categories based on approximate global geometric correspondence. This technique works by partitioning the image into increasingly fine sub-regions and computing histograms of local features found inside each sub-region. The resulting "spatial pyramid" is a simple and computationally efficient extension of an orderless bag-of-features image representation, and it shows significantly improved performance on challenging scene categorization tasks. Specifically, our proposed method exceeds the state of the art on the Caltech-101 database and achieves high accuracy on a large database of fifteen natural scene categories. The spatial pyramid framework also offers insights into the success of several recently proposed image descriptions, including Torralbas "gist" and Lowes SIFT descriptors.
8,736 citations
"Artistic ideation based on computer..." refers background or methods in this paper
...[7] advocates an approach that has the advantage of maintaining continuity with the popular ”visual vocabulary” paradigm....
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...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]....
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...To overcome the limitations of the bag-of-visterms approach, a spatial pyramid matching scheme was introduced in [8] and [7]....
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20 Jun 2005
TL;DR: This work proposes a novel approach to learn and recognize natural scene categories by representing the image of a scene by a collection of local regions, denoted as codewords obtained by unsupervised learning.
Abstract: We propose a novel approach to learn and recognize natural scene categories. Unlike previous work, it does not require experts to annotate the training set. We represent the image of a scene by a collection of local regions, denoted as codewords obtained by unsupervised learning. Each region is represented as part of a "theme". In previous work, such themes were learnt from hand-annotations of experts, while our method learns the theme distributions as well as the codewords distribution over the themes without supervision. We report satisfactory categorization performances on a large set of 13 categories of complex scenes.
3,920 citations
"Artistic ideation based on computer..." refers methods in this paper
...Our decision to use a dense regular grid instead of interest points was based on the comparative evaluation of [3], who have shown that dense features work better for scene classification....
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17 Jun 2006
TL;DR: A large-scale evaluation of an approach that represents images as distributions of features extracted from a sparse set of keypoint locations and learns a Support Vector Machine classifier with kernels based on two effective measures for comparing distributions, the Earth Mover’s Distance and the χ2 distance.
Abstract: Recently, methods based on local image features have shown promise for texture and object recognition tasks. This paper presents a large-scale evaluation of an approach that represents images as distributions (signatures or histograms) of features extracted from a sparse set of keypoint locations and learns a Support Vector Machine classifier with kernels based on two effective measures for comparing distributions, the Earth Movers Distance and the ÷2 distance. We first evaluate the performance of our approach with different keypoint detectors and descriptors, as well as different kernels and classifiers. We then conduct a comparative evaluation with several state-of-the-art recognition methods on 4 texture and 5 object databases. On most of these databases, our implementation exceeds the best reported results and achieves comparable performance on the rest. Finally, we investigate the influence of background correlations on recognition performance.
1,863 citations
17 Oct 2005
TL;DR: A new fast kernel function is presented which maps unordered feature sets to multi-resolution histograms and computes a weighted histogram intersection in this space and is shown to be positive-definite, making it valid for use in learning algorithms whose optimal solutions are guaranteed only for Mercer kernels.
Abstract: Discriminative learning is challenging when examples are sets of features, and the sets vary in cardinality and lack any sort of meaningful ordering. Kernel-based classification methods can learn complex decision boundaries, but a kernel over unordered set inputs must somehow solve for correspondences epsivnerally a computationally expensive task that becomes impractical for large set sizes. We present a new fast kernel function which maps unordered feature sets to multi-resolution histograms and computes a weighted histogram intersection in this space. This "pyramid match" computation is linear in the number of features, and it implicitly finds correspondences based on the finest resolution histogram cell where a matched pair first appears. Since the kernel does not penalize the presence of extra features, it is robust to clutter. We show the kernel function is positive-definite, making it valid for use in learning algorithms whose optimal solutions are guaranteed only for Mercer kernels. We demonstrate our algorithm on object recognition tasks and show it to be accurate and dramatically faster than current approaches
1,669 citations
"Artistic ideation based on computer..." refers background or methods in this paper
...As introduced in [8], a pyramid match kernel works with an orderless image representation....
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...In summary, both the histogram intersection and the pyramid match kernel are Mercer kernels [8]....
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...To overcome the limitations of the bag-of-visterms approach, a spatial pyramid matching scheme was introduced in [8] and [7]....
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...Putting all the pieces together, the pyramid match kernel [8] is defined by...
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