Below is an abstract from a computer science research paper. Given this abstract, your task is to extract all unique relationships between entities of the following types:

- "USED-FOR": For instance, "Our method models user proficiency" -> ["method", "user proficiency", "USED-FOR"].
- "EVALUATE-FOR": For instance, "We evaluate our method using F1-measure" -> ["F1-measure", "method", "EVALUATE-FOR"].
- "FEATURE-OF": For instance, "prior knowledge of the model" -> ["prior knowledge", "model", "FEATURE-OF"].
- "HYPONYM-OF": For instance, "TUIT is a software library" -> ["TUIT", "software library", "HYPONYM-OF"].
- "PART-OF": For instance, "We incorporate NLU module into the system." -> ["NLU module", "system", "PART-OF"].
- "COMPARE": For instance, "Unlike the quantitative prior, the qualitative prior is often ignored" -> ["quantitative prior", "qualitative prior", "COMPARE"].
- "CONJUNCTION": For instance: "obtained from human expert or knowledge base" -> ["human expert", "knowledge base", "CONJUNCTION"].

Format your output as a json array. Each entry in the array should express a single relation, formatted as ["<Entity_A>", "<Entity_B>", "<RELATION_A_B>"]. An example output might look like:

[["neural networks", "classification", "USED-FOR"], ["neuron", "neural network", "PART-OF"]].

If no relations are found, please return an empty array [].

Abstract:

We present a new approach for building an efficient and robust classifier for the two class problem, that localizes objects that may appear in the image under different orien-tations. In contrast to other works that address this problem using multiple classifiers, each one specialized for a specific orientation, we propose a simple two-step approach with an estimation stage and a classification stage. The estimator yields an initial set of potential object poses that are then validated by the classifier. This methodology allows reducing the time complexity of the algorithm while classification results remain high. The classifier we use in both stages is based on a boosted combination of Random Ferns over local histograms of oriented gradients (HOGs), which we compute during a pre-processing step. Both the use of supervised learning and working on the gradient space makes our approach robust while being efficient at run-time. We show these properties by thorough testing on standard databases and on a new database made of motorbikes under planar rotations, and with challenging conditions such as cluttered backgrounds, changing illumination conditions and partial occlusions.
[["local histograms of oriented gradients (HOGs)", "boosted combination of Random Ferns", "FEATURE-OF"], ["motorbikes under planar rotations", "database", "FEATURE-OF"], ["time complexity", "approach", "EVALUATE-FOR"], ["estimation stage", "classification stage", "CONJUNCTION"], ["changing illumination conditions", "conditions", "HYPONYM-OF"], ["changing illumination conditions", "partial occlusions", "CONJUNCTION"], ["classification stage", "object poses", "USED-FOR"], ["gradient space", "approach", "USED-FOR"], ["classification stage", "class problem", "USED-FOR"], ["conditions", "database", "FEATURE-OF"], ["cluttered backgrounds", "changing illumination conditions", "CONJUNCTION"], ["approach", "classification stage", "USED-FOR"], ["estimation stage", "approach", "PART-OF"], ["supervised learning", "approach", "USED-FOR"], ["pre-processing step", "local histograms of oriented gradients (HOGs)", "USED-FOR"], ["partial occlusions", "conditions", "HYPONYM-OF"], ["boosted combination of Random Ferns", "classification stage", "USED-FOR"], ["cluttered backgrounds", "conditions", "HYPONYM-OF"], ["classification stage", "approach", "PART-OF"]]