Towards information-theoretic K-means clustering for image indexing

Information-theoretic K-means (Info-Kmeans) aims to cluster high-dimensional data, such as images featured by the bag-of-features (BOF) model, using K-means algorithm with KL-divergence as the distance. While research efforts along this line have shown promising results, a remaining challenge is to...

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Veröffentlicht in:Signal processing Jg. 93; H. 7; S. 2026 - 2037
Hauptverfasser: Cao, Jie, Wu, Zhiang, Wu, Junjie, Liu, Wenjie
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Amsterdam Elsevier B.V 01.07.2013
Elsevier
Schlagworte:
Algorithmics. Computability. Computer arithmetics
Algorithms
Applied sciences
Artificial intelligence
Centroids
Cluster analysis
Clustering
Computation
Computer science; control theory; systems
Data processing. List processing. Character string processing
Entropy
Exact sciences and technology
Information theory
Information, signal and communications theory
Information-theoretic clustering
K-means
KL-divergence
Memory organisation. Data processing
Pattern recognition. Digital image processing. Computational geometry
Sails
Software
Telecommunications and information theory
Theoretical computing
Variable Neighborhood Search (VNS)
Information-theoretic clustering
K-means
KL-divergence
Variable Neighborhood Search (VNS)
Divergence
Image processing
Iterative method
K means algorithm
Entropy
Modeling
Bag of words
Multidimensional analysis
Efficiency
Center of mass
Classification
Sparse representation
Learning algorithm
Stereo image processing
Image databank
Cluster
Experimental result
Multidimensional database
Sail
Artificial intelligence
Summation
Indexing
Information theory
ISSN:0165-1684, 1872-7557
Online-Zugang:Volltext
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Zusammenfassung:Information-theoretic K-means (Info-Kmeans) aims to cluster high-dimensional data, such as images featured by the bag-of-features (BOF) model, using K-means algorithm with KL-divergence as the distance. While research efforts along this line have shown promising results, a remaining challenge is to deal with the high sparsity of image data. Indeed, the centroids may contain many zero-value features that create a dilemma in assigning objects to centroids during the iterative process of Info-Kmeans. To meet this challenge, we propose a Summation-bAsed Incremental Learning (SAIL) algorithm for Info-Kmeans clustering in this paper. Specifically, SAIL can avoid the zero-feature dilemma by replacing the computation of KL-divergence between instances and centroids, by the computation of centroid entropies only. To further improve the clustering quality, we also introduce the Variable Neighborhood Search (VNS) meta-heuristic and propose the V-SAIL algorithm. Experimental results on various benchmark data sets clearly demonstrate the effectiveness of SAIL and V-SAIL. In particular, they help to successfully recognize nine out of 11 landmarks from extremely high-dimensional and sparse image vectors, with the presence of severe noise. ► We showed the zero-feature dilemma of the information-theoretic K-means. ► We proposed an algorithm SAIL to handle the zero-feature dilemma. ► We proposed an algorithm V-SAIL to improve clustering quality of SAIL. ► SAIL and V-SAIL show excellent performance for landmark recognition with noise.
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ISSN:0165-1684
1872-7557
DOI:10.1016/j.sigpro.2012.07.030