Download e-book for kindle: Feature Coding for Image Representation and Recognition by Yongzhen Huang, Tieniu Tan

By Yongzhen Huang, Tieniu Tan

ISBN-10: 3662449994

ISBN-13: 9783662449998

ISBN-10: 3662450003

ISBN-13: 9783662450000

This short provides a accomplished creation to add coding, which serves as a key module for the common item popularity pipeline. The textual content deals a wealthy mixture of concept and perform whereas displays the hot advancements on characteristic coding, overlaying the subsequent 5 features: (1) evaluation the state of the art, interpreting the motivations and mathematical representations of assorted function coding tools; (2) discover how numerous characteristic coding algorithms evolve alongside years; (3) Summarize the most features of normal function coding algorithms and categorize them consequently; (4) speak about the purposes of characteristic coding in numerous visible projects, learn the effect of a few key components in characteristic coding with extensive experimental stories; (5) give you the feedback of ways to use diverse function coding equipment and forecast the capability instructions for destiny paintings at the subject. it's compatible for college kids, researchers, practitioners attracted to item recognition.

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Extra resources for Feature Coding for Image Representation and Recognition

Example text

The rule here corresponds to the K-means clustering, and θ denotes the cluster centers. As OMP only involves the feature space, the corresponding pooling matrix can be re-written as: P = φ(Z, θ ). 5) where θ = (θ1 , θ2 , . . , θC ) denotes the cluster centers of features’ representation in the feature space. 2 Multiple Spatial Pooling Compared with OMP, MSP has two major differences. Firstly, the task of MSP and OMP is different. OMP is designed for exploiting the structure of the feature space, while MSP is proposed for modeling global spatial structure.

Namely, this penalty function forces the input features to be similar to some codewords not only in source pixel space but also in attributes features space. 4 Local Tangent-Based Coding Local tangent-based coding [24] assumes that all features constitute a smooth manifold where codewords are also located. Feature coding is then interpreted as manifold approximation using the codewords. In this way, features are not independent but closely related, expressed by a Lipschitz smooth function. The main components in local tangent-based coding are manifold approximation and intrinsic dimensionality estimation, which are introduced respectively as follows.

Zhou, K. Yu, T. Zhang, T. Huang, Image classification using super-vector coding of local image descriptors, in European Conference on Computer Vision (2010) Chapter 5 Experimental Study of Feature Coding Abstract The previous several chapters discussed feature coding from the viewpoint of theoretical analysis. In this chapter, we will provide experimental analysis for different categories of feature coding algorithms. Firstly, we will introduce several classic databases (involving image classification, object categorization and scene recognition) and the corresponding experimental set-ups.

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Feature Coding for Image Representation and Recognition by Yongzhen Huang, Tieniu Tan


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