2-D Shape Classification Using Hidden Markov Model
IEEE Transactions on Pattern Analysis and Machine Intelligence
IEEE Transactions on Pattern Analysis and Machine Intelligence
Shape Matching and Object Recognition Using Shape Contexts
IEEE Transactions on Pattern Analysis and Machine Intelligence
A symbolic representation of time series, with implications for streaming algorithms
DMKD '03 Proceedings of the 8th ACM SIGMOD workshop on Research issues in data mining and knowledge discovery
Shape Similarity Index for Time Series based on Features of Euclidean Distances Histograms
CIC '06 Proceedings of the 15th International Conference on Computing
Scalable music recommendation by search
Proceedings of the 15th international conference on Multimedia
Compact representation of contours using directional grid chain code
Image Communication
Improving Web search using image snippets
ACM Transactions on Internet Technology (TOIT)
Extracting relevant snippets for web navigation
AAAI'08 Proceedings of the 23rd national conference on Artificial intelligence - Volume 2
Collaborative text-annotation resource for disease-centered relation extraction from biomedical text
Journal of Biomedical Informatics
Retrieval by shape similarity with perceptual distance andeffective indexing
IEEE Transactions on Multimedia
Shape Indexing and Recognition Based on Regional Analysis
IEEE Transactions on Multimedia
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In most existing works on shape contour matching, the shape contours are considered and matched in whole. When searching for contour snippets, however, techniques that match whole contours are not directly applicable. In particular, a relevant snippet can be anywhere on a shape contour; moreover, the relevance of shape snippet is a function of not only the shape of the snippet itself, but also of its neighborhood on the contour. In this paper, we propose an HMM based solution to shape snippet extraction. Relying on a general-purpose symbolic representation (such as SAX), we first convert the shape contour onto a representation suitable for snippet marking and extraction processes. We then show that, given a set of samples, we can train an HMM capable of detecting relevant snippets in new shape images. Next, we show that the HMM performance can be boosted significantly if the similarities between the symbolic representations are used to create new sibling training sequences from the input sequences. The experiment results show that just adding one additional sibling per input training sequence can improve the diversity of the training set sufficiently to boost the overlaps between actual and detected snippets much. While a naive application of this metadata driven training technique can increase the training costs significantly, we show that a novel metadata-driven HMM (mHMM) scheme can significantly improve the HMM-base snippet detection performance with negligible costs.