This paper proposes a method based on the bag-of-words (BoW) and the softmax regression for microscopic image classification. Essentially, the locality-constrained linear coding (LLC) is adopted for local feature encoding. Compared with the traditionally adopted vector quantization (VQ) in the BoW framework, the LLC encodes local structures of microscopic images with lower quantization errors and generates a sparse image representation. This enables the use of linear classifiers with low computational complexity. A softmax regression classifier is then adopted to address the multi-categorical classification task where the confidence of categorical prediction is quantified by posterior probabilities. Compared with other linear classifiers (such as the linear SVM) which only assign labels to images, such probabilistic outputs provide extra quantitative information to analyze misclassified images. Our experiments on the 2D-Hela and the PAP smear data sets show significant performance improvement of the proposed method comparing with competing methods using different features and classifiers under the BoW framework.
|Title of host publication||IEEE International Symposium on Circuits and Systems|
|Subtitle of host publication||From Dreams to Innovation, ISCAS 2017 - Conference Proceedings|
|Publisher||Institute of Electrical and Electronics Engineers Inc.|
|Publication status||Published - 2017 Sept 25|
|Event||50th IEEE International Symposium on Circuits and Systems, ISCAS 2017 - Baltimore, United States|
Duration: 2017 May 28 → 2017 May 31
|Name||Proceedings - IEEE International Symposium on Circuits and Systems|
|Other||50th IEEE International Symposium on Circuits and Systems, ISCAS 2017|
|Period||17/5/28 → 17/5/31|
Bibliographical noteFunding Information:
This work was partly supported by National NSF of China (No. 61673059, 61503104).
© 2017 IEEE.
All Science Journal Classification (ASJC) codes
- Electrical and Electronic Engineering