Human free-hand sketches have been studied in various contexts including sketch recognition, synthesis and finegrained sketch-based image retrieval (FG-SBIR). A fundamental challenge for sketch analysis is to deal with drastically different human drawing styles, particularly in terms of abstraction level. In this work, we propose the first stroke-level sketch abstraction model based on the insight of sketch abstraction as a process of trading off between the recognizability of a sketch and the number of strokes used to draw it. Concretely, we train a model for abstract sketch generation through reinforcement learning of a stroke removal policy that learns to predict which strokes can be safely removed without affecting recognizability. We show that our abstraction model can be used for various sketch analysis tasks including: (1) modeling stroke saliency and understanding the decision of sketch recognition models, (2) synthesizing sketches of variable abstraction for a given category, or reference object instance in a photo, and (3) training a FG-SBIR model with photos only, bypassing the expensive photo-sketch pair collection step.
Hair highly characterises human appearance. Hair detection in images is useful for many applications, such as face and gender recognition, video surveillance, and hair modelling. We tackle the problem of hair analysis (detection, segmentation, and hairstyle classification) from unconstrained view by relying only on textures, without a-priori information on head shape and location, nor using body-part classifiers. We first build a hair probability map by classifying overlapping patches described by features extracted from a CNN, using Random Forest. Then modelling hair (resp. non-hair) from high (resp. low) probability regions, we segment at pixel level uncertain areas by using LTP features and SVM. For the experiments we extend Figaro, an image database for hair detection to Figaro1k, a new version with more than 1,000 manually annotated images. Achieved segmentation accuracy (around 90%) is superior to known state-of-the-art. Images are eventually classified into hairstyle classes: straight, wavy, curly, kinky, braids, dreadlocks, and short.
Hair is one of the elements that mostly characterize people appearance. Being able to detect hair in images can be useful in many applications, such as face recognition, gender classification, and video surveillance. To this purpose we propose a novel multi-class image database for hair detection in the wild, called Figaro. We tackle the problem of hair detection without relying on a-priori information related to head shape and location. Without using any human-body part classifier, we first classify image patches into hair vs. non-hair by relying on Histogram of Gradients (HOG) and Linear Ternary Pattern (LTP) texture features in a random forest scheme. Then we obtain results at pixel level by refining classified patches by a graph-based multiple segmentation method. Achieved segmentation accuracy (85%) is comparable to state-of-the-art on less challenging databases.
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