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ABSTRACT
I. INTRODUCTION
II. PRELIMINARIES
III. WAVELET PACKET BASED TEXTURE RETRIEVAL
IV. WAVELET PACKET BASIS SELECTION
V. SUMMARY OF THE MODELING STAGE
VI. EXPERIMENTAL ANALYSI
VII. DISCUSSION: CONNECTION WITH CNN
VIII. CONCLUSION AND FUTURE WORK
APPENDIX WAVELET PACKETS ANALYSIS
ACKNOWLEDGMENT
REFERENCES
ABSTRACT
Wavelet Packets (WPs) bases are explored seeking new discriminative features for texture indexing. The task of WP feature design is formulated as a learning decision problem by selecting the filter-bank structure of a basis (within a WPs family) that offers an optimal balance between estimation and approximation errors. To address this problem, a computationally efficient algorithm is adopted that uses the tree-structure of the WPs collection and the Kullback-Leibler divergence as a discrimination criterion. The adaptive nature of the proposed solution is demonstrated in synthetic and real data scenarios. With synthetic data, we demonstrate that the proposed features can identify discriminative bands, which is not possible with standard wavelet decomposition. With data with real textures, we show performance improvements with respect to the conventional Wavelet-based decomposition used under the same conditions and model assumptions.
INTRODUCTION
In the current information age, we have access to unprecedented sources of digital image content. Consequently, being able to index and organize these documents based solely on the content extracted from the signals without relying on metadata or expensive human annotations has become a central problem [1]–[21]. In this context, an important task in image processing is texture retrieval. This problem has been richly studied over the last two decades with different frameworks and approaches [3]–[21], including, more recently, deep learning approaches [22], [22]–[26], [26]–[29]. In a nutshell, the texture retrieval problem can be formulated in two stages. The first stage, feature extraction (FE), implies the creation of low-dimensional descriptions of the image (i.e., the dimensionality reduction phase) with the objective of capturing the semantic high-level information that discriminates relevant texture classes. The second stage proposes a similarity measure (SM) on the feature space to compare and organize the images in terms of their signal content. For the FE stage, the Wavelet transform (WT) has been widely adopted as a tool to decompose and organize the signal content in sub-spaces associated with different levels of resolution (or scale) information [30], [31]. Based on this sub-space decomposition, energy features have been used as a signature that represents the salient texture attributes for texture indexing [32].