Combining Pyramid Match Kernel and Spatial Pyramid for Image Classification

Karmakar, Purnendu; Teng, Shyh Wei; Zhang, Dengsheng; Liu, Ying; Lu, Guojun

doi:10.1109/dicta.2016.7797022

Cited by 1 publication

(1 citation statement)

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“…By applying k-means clustering on the training-image descriptor sets, separate dictionaries for each database are formed. To increase the discriminative power of image representation [33], the dictionary size of each database considered is 1000. Next, all the images of each database are partitioned by both RI and traditional schemes.…”

Section: B Descriptor Extraction and Dictionary Constructionmentioning

confidence: 99%

An Enhancement to the Spatial Pyramid Matching for Image Classification and Retrieval

Karmakar

Teng

et al. 2020

IEEE Access

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Spatial pyramid matching (SPM) is one of the widely used methods to incorporate spatial information into the image representation. Despite its effectiveness, the traditional SPM is not rotation invariant. A rotation invariant SPM has been proposed in the literature but it has many limitations regarding the effectiveness. In this paper, we investigate how to make SPM robust to rotation by addressing those limitations. In an SPM framework, an image is divided into an increasing number of partitions at different pyramid levels. In this paper, our main focus is on how to partition images in such a way that the resulting structure can deal with image-level rotations. To do that, we investigate three concentric ring partitioning schemes. Apart from image partitioning, another important component of the SPM framework is a weight function. To apportion the contribution of each pyramid level to the final matching between two images, the weight function is needed. In this paper, we propose a new weight function which is suitable for the rotation-invariant SPM structure. Experiments based on image classification and retrieval are performed on five image databases. The detailed result analysis shows that we are successful in enhancing the effectiveness of SPM for image classification and retrieval. INDEX TERMS Spatial pyramid matching, rotation invariance, image classification, image retrieval.

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Section: B Descriptor Extraction and Dictionary Constructionmentioning

confidence: 99%