LOCAL FEATURE REPRESENTATION FOR IMAGE RECOGNITION

摘要

Techniques are disclosed for image feature representation. The techniques exhibit discriminative power that can be used in any number of classification tasks, and are particularly effective with respect to fine-grained image classification tasks. In an embodiment, a given image to be classified is divided into image patches. A vector is generated for each image patch. Each image patch vector is compared to the Gaussian mixture components (each mixture component is also a vector) of a Gaussian Mixture Model (GMM). Each such comparison generates a similarity score for each image patch vector. For each Gaussian mixture component, the image patch vectors associated with a similarity score that is too low are eliminated. The selectively pooled vectors from all the Gaussian mixture components are then concatenated to form the final image feature vector, which can be provided to a classifier so the given input image can be properly categorized.

主权项

1. A computer-implemented method, comprising:
receiving a digital image; dividing the image into image patches; generating a vector for each image patch; comparing each image patch vector to Gaussian mixture components of a Gaussian Mixture Model (GMM), each mixture component being a vector, thereby generating a similarity score for each image patch vector; for each Gaussian mixture component, eliminating one or more image patch vectors associated with a similarity score that is below a given threshold; and generating a final image feature vector from the remaining image patch vectors of all the Gaussian mixture components.