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DeviantART Analysis using Image Features

DeviantART Analysis using Image Features. Bart Buter, Davide Modolo, Sander van Noort Nick Dijkshoorn, Quang Nguyen, Bart van de Poel. Profile Project . Our project focused on explorative research on the analysis of artists and their images of a huge art community called deviantART

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DeviantART Analysis using Image Features

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  1. DeviantART Analysis using Image Features Bart Buter, Davide Modolo, Sander van Noort Nick Dijkshoorn, Quang Nguyen, Bart van de Poel

  2. Profile Project • Our project focused on explorative research on the analysis of artists and their images of a huge art community called deviantART • The research touched different fields: • Visualization (implementation of a Toolkit) • Data collection • Features extraction (statistical and cognitive-inspired) • Classification • Network analysis

  3. Overview • Introduction • Toolkit • Experiments & Results • Future work • Conclusion

  4. Introduction - deviantART • deviantART (dA) is the largest online community showcasing various forms of user-made artwork • 13 million registered members (called Deviants) • Allows emerging and established artists to exhibit, promote, and share their works • All artwork is well organized (comprehensive category structure) • Traditional media (painting and sculpture), to digital art, pixel art, films and anime

  5. Research questions • Can we visualize important aspects of deviantART? • Can artists and/or styles be distinguished? • Are artists influencing each other? • Do art styles change over time? • Are there none-artists interesting for deviantART?

  6. Toolkit • General tool to answer research questions about social art communities (deviantART) • 4 Components Online

  7. Data collection from deviantART • Network of “professional” artists • Download artist’s name and their watchers • Output for Pajek and Matlab graph toolbox • Artist’s images and information about these images • Download galleries from users as dataset • No web API, instead follow Backend links • Parse RSS XML files and download images Data collection

  8. Data collection • For each image store a xml file Example: <?xml version="1.0"?> <root xml_tb_version="3.1"> <guid>http://catluvr2.deviantart.com/art/42-Journals-73664427</guid> <title>-42 Journals</title> <category>customization/screenshots/other</category> <filename>_42_Journals_by_catluvr2.jpg</filename> </root> Data collection

  9. Dataset information • Downloaded 31 users • About 5000 images • Daily Deviations of a random day • Top categories: • photography: 2244 • customization: 906 • traditional: 842 • digitalart: 587 • fanart: 239 Data collection

  10. Feature extraction • Why we need features • Can’t visualize sets of images in high-dimensional space • Features can be intuitive for toolkit users • Easier to work with than raw data (classification) • Kind of features: • Statistical features • Cognitively-inspired features Feature extraction

  11. Feature format • Store features in XML files • One XML file per image describing all features • Easy to addnew features of existing images • Easy to add images • Onlycalculate features that are notalready present in XML file • Addthose features to the XML file of the image Feature extraction

  12. Statistical features • Low level & understandablefeatures • RGB values (average, median) • Hue, Saturation&Intensityvalues (average, median) • Edge-pixel ratio • Corner-pixel ratio • Entropy of the intensity • Variance of the intensity • Compositional features Feature extraction – Statistic part

  13. Edge-pixel ratio Ratio: 0.0094 Ratio: 0.0998 Feature extraction - Statistic part

  14. Average of the intensity AvgIntensity: 21.90 AvgIntensity: 123.96 AvgIntensity: 243.67 Feature extraction - Statistic part

  15. Entropy of the intensity Intensity entropy: 1.5408 Intensity entropy: 7.8799 Feature extraction - Statistic part

  16. Variance of the intensity Intensity variance: 506 Intensity variance: 14676 Feature extraction - Statistic part

  17. Compositional edge-pixel ratio Feature extraction - Statistic part

  18. Hue and Saturation Feature extraction - Statistic part

  19. Weibull-Distribution Image Contrast • Why Feature extraction – Statistical part

  20. Cognitively-inspired features Model of Saliency-Based Visual Attention • It has appeared that attention influences visual information even in the earliest areas of primate visual cortex • This influence seems to shape an integrated saliency map • This maps is the representation of the environment that weighs every input by its local feature contrast and its current behavioral relevance • It enables the visual system to integrate alarge amount of information Feature extraction - Cognitive part

  21. Itti, Koch and Niebur’s Model Feature extraction - Cognitive part

  22. Example of saliency map color ORIGINAL IMAGE orientation Feature extraction - Cognitive part intensity EXTRA: skin SALIENCY MAP

  23. What do we have Intensity map Orientation map • Important visual features about the style of the photo of this image: • - The portrait is not exactly in the middle • The portrait is a human • - The portrait is standing statically • - Colors are quite uniform, and they are not so many Skin map Feature extraction - Cognitive part Saliency map Color map But how to use all the different maps to represent these information?

  24. Cognitively-inspired features (1) • Shannon entropy of the 5 different maps (the saliency and the conspicuity ones) • Standard deviation of the saliency distribution in the saliency map • Location of the three most salient points • Skin intensity Feature extraction - Cognitive part

  25. Cognitively-inspired features (2) • Location has been computed using the Inhibition Of Return (IOR) procedure: Original saliency map Feature extraction - Cognitive part 3 most salient locations After the first inhibition After the second inhibition

  26. Cognitively-inspired features (3) • Skin is an extra channel (not standard in the Itti’s model) but it has been found really interesting • It can easily be used to detect nude images (that are quite popular within devianArt’s professional photographer) Original image Feature extraction - Cognitive part Skin map Skin map Original image

  27. OpenCV face detector Feature extraction - Cognitive part

  28. Classification • Given a set of features, the classification is used to: • Determine if two artists/categories are distinguishable • Determine which features are useful to do it • Different classifiers are available in the Toolkit: • k-Nearest Neighbour (kNN) • Naive Bayes (NB) • Nearest Mean (NM) • Support Vector Machine (libSVM) Classification

  29. Classification • Pre-processing functions: • Reading in XML files and creating a dataset • Normalization • Dataset filtering on classes and features • Parameter optimization using cross-validation • Classification current capabilities: • 1 class against another class • 1 class against all other classes Classification

  30. Classification • Feature selection is needed when dealing with a lot of features • Reduces the dimensions of the data representation • Give the feature combination that best separate a class • Sequential forward feature selection • First select the most informative feature and iteratively add the next most informative feature to it • Criterion is based on the inter-intra distance Classification

  31. Classification • Evaluation measures: • Precision • The percentage of how many of the positive classified images were indeed positive • Recall • The percentage of how many of the total positive images were found positive • F1-Measure • The weighted average of the precision and recall Classification

  32. Visualization • Purpose of the visualization: • Visualize the dataset • Find patterns • Analyse classification results • Filtering (relevant information) • Input: Dataset (thumbs+full) images & XML features files • Converted to single TAB seperated file • Express the classification performance • Capture the performance in one graph • Input: performance output of the classifier Visualization

  33. Visualization • Use existing visualization application? • Mondrian, general purpose statistical data-visualization system Visualization http://rosuda.org/mondrian/

  34. Visualization • Use existing visualization application? • XmdvTool, interactive visual exploration of multivariate data sets • Flat version of the data set Visualization http://davis.wpi.edu/~xmdv/

  35. Visualization • Use existing visualization application? • Tool that has generic uses, produce only generic displays • Data can take many interesting forms • Require unique types of display and interaction • Not captured with general applications • UI not intuitive (lack easy way to filter data) • (These tools also look outdated) Visualization

  36. Visualization • What language/framework for our visualization? • There are many… • Prefuse visualization toolkit (generic displays) • Adobe Flash/Flex (expensive, slow for large datasets) Visualization

  37. Visualization • (Partially) Implemented in “Processing” • Open source programming language to create images, animations, and interactions • Build on top of Java (collection of Java classes) • Consists of: • Processing Development Environment (PDE) (very minimalistic) • A collection of commands (API) • Several libraries that support more advanced features (OpenGL, XML) • Easy to integrate into Java (Eclipse) Visualization

  38. Visualization: Processing • Provides functions to make life more easy • image(img, x, y, [width, height]) • line(x1, y1, x2, y2) stroke(color) • Not to draw complete graphs/plots • Right combination of cost, ease of use and speed • Export the application as a Java Applet • Run it on a website • Use URL instead of images to avoid legal issues Visualization

  39. Experiments & Results

  40. Experiment #1 – Classification • Goal: • Use the toolkit to find what kind of features best separate two artists • Details of the experiment • Experiment was performed for all artists in the dataset • Feature selection algorithm was used to output the 1-5 most informative features • Evaluation was done using the F-measure

  41. Selecting the classifier • Select classifier for the experiment • Train all the classifiers on a subset of the trainingdata using crossvalidation to optimize parameters • Criteria of selection: F-measure • SVM gives the highest F-measure Average F-measure 1vs1 classification over all artists

  42. Result Matrix using the top 1 feature

  43. Result Matrix using top 2 features

  44. Result Matrix using top 3 features

  45. Result Matrix using the top 4 features

  46. Result Matrix using the top 5 features

  47. Result Matrix using all features

  48. Visualization Case (1) • Artist Pair: Kitsunebaka91 and LALAax • Fmeasure Pair: 0.952941 and 0.884615 • medIntCells_2 • gridEdgeRatio_4 • Artist Pair: fediaFedia and gsphoto • Fmeasure Pair: 0.867347 and 0.938095 • avgHue • intVariance

  49. Visualization Case (2) • Artist Pair: K1lgore and sekcyjny • Fmeasure Pair: 0.692308 and 0.640000 • avgBCells_3 • salMapCEntropy • Artist Pair: stereoflow and zihnisinir • Fmeasure Pair: 0.649007 and 0.683871 • avgHueCells_4 • avgR

  50. Results

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