Training-Free, Generic Object Detection Using Locally Adaptive Regression Kernels
- 18 August 2009
- journal article
- research article
- Published by Institute of Electrical and Electronics Engineers (IEEE) in IEEE Transactions on Pattern Analysis and Machine Intelligence
- Vol. 32 (9), 1688-1704
- https://doi.org/10.1109/tpami.2009.153
Abstract
We present a generic detection/localization algorithm capable of searching for a visual object of interest without training. The proposed method operates using a single example of an object of interest to find similar matches, does not require prior knowledge (learning) about objects being sought, and does not require any preprocessing step or segmentation of a target image. Our method is based on the computation of local regression kernels as descriptors from a query, which measure the likeness of a pixel to its surroundings. Salient features are extracted from said descriptors and compared against analogous features from the target image. This comparison is done using a matrix generalization of the cosine similarity measure. We illustrate optimality properties of the algorithm using a naive-Bayes framework. The algorithm yields a scalar resemblance map, indicating the likelihood of similarity between the query and all patches in the target image. By employing nonparametric significance tests and nonmaxima suppression, we detect the presence and location of objects similar to the given query. The approach is extended to account for large variations in scale and rotation. High performance is demonstrated on several challenging data sets, indicating successful detection of objects in diverse contexts and under different imaging conditions.Keywords
This publication has 51 references indexed in Scilit:
- Static and space-time visual saliency detection by self-resemblanceJournal of Vision, 2009
- Matrix comparison, Part 1: Motivation and important issues for measuring the resemblance between proximity measures or ordination resultsJournal of the American Society for Information Science and Technology, 2007
- Learning generative visual models from few training examples: An incremental Bayesian approach tested on 101 object categoriesComputer Vision and Image Understanding, 2007
- A Comparison of Some Tests for Determining the Number of Nonzero Canonical CorrelationsCommunications in Statistics - Simulation and Computation, 2006
- Learning to detect objects in images via a sparse, part-based representationIeee Transactions On Pattern Analysis and Machine Intelligence, 2004
- Requirements for a cocitation similarity measure, with special reference to Pearson's correlation coefficientJournal of the American Society for Information Science and Technology, 2003
- Shape matching and object recognition using shape contextsIeee Transactions On Pattern Analysis and Machine Intelligence, 2002
- Nonlinear Dimensionality Reduction by Locally Linear EmbeddingScience, 2000
- Neural network-based face detectionIeee Transactions On Pattern Analysis and Machine Intelligence, 1998
- Thirteen Ways to Look at the Correlation CoefficientThe American Statistician, 1988