2 research outputs found
Object-centric Sampling for Fine-grained Image Classification
This paper proposes to go beyond the state-of-the-art deep convolutional
neural network (CNN) by incorporating the information from object detection,
focusing on dealing with fine-grained image classification. Unfortunately, CNN
suffers from over-fiting when it is trained on existing fine-grained image
classification benchmarks, which typically only consist of less than a few tens
of thousands training images. Therefore, we first construct a large-scale
fine-grained car recognition dataset that consists of 333 car classes with more
than 150 thousand training images. With this large-scale dataset, we are able
to build a strong baseline for CNN with top-1 classification accuracy of 81.6%.
One major challenge in fine-grained image classification is that many classes
are very similar to each other while having large within-class variation. One
contributing factor to the within-class variation is cluttered image
background. However, the existing CNN training takes uniform window sampling
over the image, acting as blind on the location of the object of interest. In
contrast, this paper proposes an \emph{object-centric sampling} (OCS) scheme
that samples image windows based on the object location information. The
challenge in using the location information lies in how to design powerful
object detector and how to handle the imperfectness of detection results. To
that end, we design a saliency-aware object detection approach specific for the
setting of fine-grained image classification, and the uncertainty of detection
results are naturally handled in our OCS scheme. Our framework is demonstrated
to be very effective, improving top-1 accuracy to 89.3% (from 81.6%) on the
large-scale fine-grained car classification dataset
N-ary Error Correcting Coding Scheme
The coding matrix design plays a fundamental role in the prediction
performance of the error correcting output codes (ECOC)-based multi-class task.
{In many-class classification problems, e.g., fine-grained categorization, it
is difficult to distinguish subtle between-class differences under existing
coding schemes due to a limited choices of coding values.} In this paper, we
investigate whether one can relax existing binary and ternary code design to
-ary code design to achieve better classification performance. {In
particular, we present a novel -ary coding scheme that decomposes the
original multi-class problem into simpler multi-class subproblems, which is
similar to applying a divide-and-conquer method.} The two main advantages of
such a coding scheme are as follows: (i) the ability to construct more
discriminative codes and (ii) the flexibility for the user to select the best
for ECOC-based classification. We show empirically that the optimal
(based on classification performance) lies in with some trade-off in
computational cost. Moreover, we provide theoretical insights on the dependency
of the generalization error bound of an -ary ECOC on the average base
classifier generalization error and the minimum distance between any two codes
constructed. Extensive experimental results on benchmark multi-class datasets
show that the proposed coding scheme achieves superior prediction performance
over the state-of-the-art coding methods.Comment: Under submission to IEEE Transaction on Information Theor