A Comparison of Wavelet Based Spatio-temporal Decomposition Methods for Dynamic Texture Recognition

Dubois, Sloven; Péteri, Renaud; Ménard, Michel

doi:10.1007/978-3-642-02172-5_41

Cited by 22 publications

(24 citation statements)

References 14 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In [69], three dimensional wavelet energies were used as features for textures. A comparison of different wavelet filtering based approaches, that includes purely spatial, purely temporal and spatio-temporal wavelet filtering, is given in [28].…”

Section: Transform Based Modelingmentioning

confidence: 99%

See 1 more Smart Citation

Perceptual Texture Similarity for Machine Intelligence Applications

Naser

Ricordel

Callet

2017

Visual Content Indexing and Retrieval With Psycho-Visual Models

View full text Add to dashboard Cite

Textures are homogeneous visual phenomena commonly appearing in the visual scene. They are usually characterized by randomness with some stationarity. They have been well studied in different domains, such as neuroscience, vision science and computer vision, and showed an excellent performance in many applications for machine intelligence. This book chapter focuses on a special analysis task of textures for expressing texture similarity. This is quite a challenging task, because the similarity highly deviates from point-wise comparison. Texture similarity is key tool for many machine intelligence applications, such as recognition, classification, synthesis and etc. The chapter review the theories of texture perception, and provides a survey about the up-to-date approaches for both static and dynamic textures similarity. The chapter focuses also on the special application of texture similarity in image and video compression, providing the state of the art and prospects

show abstract

Section: Transform Based Modelingmentioning

confidence: 99%

“…5. Dynamic textures are spatially and temporally repetitive patterns like trees waving in the wind, water flows, fire, smoke phenomena, rotational motions [28].…”

Section: Introductionmentioning

confidence: 99%

Perceptual Texture Similarity for Machine Intelligence Applications

Naser

Ricordel

Callet

2017

Visual Content Indexing and Retrieval With Psycho-Visual Models

View full text Add to dashboard Cite

show abstract

“…One of them uses the multi-resolution wavelet transform in the space 2D+T. The method provides an acceptable recognition rate on all databases [3]. When looking at the dimensions of vector characteristics, depending on the multi-resolution method employed, we observe that for some methods, the number of descriptors is greater than the number of samples to classify.…”

Section: Construction Of Indexing Features Vectorsmentioning

confidence: 99%

“…A flag in the wind, a field of waving grass, the waves of the sea, the lake surface, the movement of the drill, smoke, fire, an ant colony, the wings of a windmill turn, fountains, waterfalls, are all examples of dynamic textures presented in the literature [3,4,5].…”

Section: Introductionmentioning

confidence: 99%

A 3D Approach to Co-Occurrence Matrix Features Used in Dynamic Textures Indexing

Mocofan

Vasiu

2013

AEF

View full text Add to dashboard Cite

Abstract. The area of applications of dynamic texture is increasingly wide: video surveillance, transaction systems, medical application and video synthesis. The paper presents an indexing model in large databases of dynamics texture using the co-occurrence matrix features. The data from the video sequence that represents the dynamic texture are loaded in a 3D matrix. The application of the co-occurrence matrix is performed for each frame of the data parallelepiped covered in 3 directions. This enables/facilitates the integration of the temporal features of the dynamic texture in the mathematical description of the behaviour. Additionally, we use more translations to compute the indexing vector from the 2D+T space of dynamic textures.

show abstract

“…In past decades, a variety of different approaches have been proposed for recognition of the DTs, such as the Linear Dynamic System (LDS) methods (Ravichandran et al, 2013), GIST method (Oliva and Torralba, 2001), the Local Binary Pattern (LBP) methods (Zhao and Pietikainen, 2007a), wavelet methods (Dubois et al, 2009;Dubois et al, 2015), morphological methods (Dubois et al, 2012), deep multilayer networks Arashloo et al, 2017), among others.…”

Section: Introductionmentioning

confidence: 99%

Convolutional Recognition of Dynamic Textures With Preliminary Categorization

Favorskaya

Pyataeva

2017

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

View full text Add to dashboard Cite

ABSTRACT:Dynamic Texture (DT) can be considered as an extension of the static texture additionally comprising the motion features. The DT is very wide but the weak studied type of textures that is employed in many tasks of computer vision. The proposed method of the DTs recognition includes a preliminary categorization based on the proposed four categories, such as natural particles with periodic movement, natural translucency/transparent non-rigid blobs with randomly changed movement, man-made opaque rigid objects with periodic movement, and man-made opaque rigid objects with stationary or chaotic movement. Such formulation permitted to construct the separate spatial and temporal Convolutional Neural Networks (CNNs) for each category. The inputs of the CNNs are a pair of successive frames (taken through 1, 2, 3, or 4 frames according to a category), while the outputs store the sets of binary features in a view of histograms. In test stage, the concatenated histograms are compared with the histograms of the classes using the Kullback-Leibler distance. The experiments demonstrate the efficiency of the designed CNNs and provided the recognition rates up 97.46-98.32% for the sequences with a single type of the DT conducted on the DynTex database.

show abstract

A Comparison of Wavelet Based Spatio-temporal Decomposition Methods for Dynamic Texture Recognition

Cited by 22 publications

References 14 publications

Perceptual Texture Similarity for Machine Intelligence Applications

Perceptual Texture Similarity for Machine Intelligence Applications

A 3D Approach to Co-Occurrence Matrix Features Used in Dynamic Textures Indexing

Convolutional Recognition of Dynamic Textures With Preliminary Categorization

Contact Info

Product

Resources

About