We present an approach for classifying chart images with sparse coding. Three chart categories are considered:
bar charts, pie charts and line graphs. We introduce the Laplacian of Gaussian (LoG) to smooth noise in the
image and detect candidate regions of interest. Noting that charts typically contain both text and graphics, we
identify text and graphic regions and learn informative features from them. Each image is then represented by
a feature vector, which can be used to learn a sparse representation via the dictionary learning algorithm for
classification. We evaluate the proposed systematic approach by a set of charts drawn from the internet. The
encouraging results certifies the proposed method.
Sparse representation based classification (SRC) has achieved state-of-the-art results on face recognition. It is
hence desired to extend its power to a broader range of classification tasks in pattern recognition. SRC first
encodes a query sample as a linear combination of a few atoms from a predefined dictionary. It then identifies the
label by evaluating which class results in the minimum reconstruction error. The effectiveness of SRC is limited
by an important assumption that data points from different classes are not distributed along the same radius
direction. Otherwise, this approach will lose their discrimination ability, even though data from different classes
are actually well-separated in terms of Euclidean distance. This assumption is reasonable for face recognition as
images of the same subject under different intensity levels are still considered to be of same-class. However, the
assumption is not always satisfied when dealing with many other real-world data, e.g., the Iris dataset, where
classes are stratified along the radius direction. In this paper, we propose a new coding strategy, called Nearest-
Farthest Neighbors based SRC (NF-SRC), to effectively overcome the limitation within SRC. The dictionary is
composed of both the Nearest Neighbors and the Farthest Neighbors. While the Nearest Neighbors are used
to narrow the selection of candidate samples, the Farthest Neighbors are employed to make the dictionary
more redundant. NF-SRC encodes each query signal in a greedy way similar to OMP. The proposed approach
is evaluated over extensive experiments. The encouraging results demonstrate the feasibility of the proposed
method.
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