Problem Statement

In multiclass object classification, a key element is to design a robust identification of relevant class specification in presence of intra-class variations. However, this is a difficult problem due to high variability of visual appearance within each class. One possible approach is to adaptively combine a set of diverse and complementary features-such as features based on color, shape-in order to discriminate each class best from all other classes [1].

Gehler and Nowzin proposed a booting approach that is to optimize jointly over a linear combination of some real valued functions (f_m). 5 fold CV is used to select the best hyper parameters for each fm individually. Intuitively, randomly split the training dataset may not yield the best training result of f_m. Torralba and Efros [2, 3] provided a comparison study using a set of popular datasets to evaluate relative data bias, cross-dataset generalization, etc. The study showed an insight that we might be able to improve training by carefully collect the datasets.

Inspired by Torralba and Efros's research, I proposed a variation of feature combination algorithm that implements data classification before the hyper parameters selection in the Gehler and Nowzin's approach. We anticipate that pre- data classification will yield a better training result in hyper parameters selection, which will ultimately improve the overall model performance in terms of accuracy.

Methods

My proposed model is a combination of feature combination for multiclass object classification and a clustered dataset, each of which has an associated weighting of stylistic features. I will address the methods for dealing with each aspect of the project individually.

Data clustering

An important problem when analyzing relational data between features is clustering, finding sets data sets that are "more similar" to each other in the dataset. Parameters of f_m of LP-beta are trained on clustered dataset individually. In light of domain adaptation approach, we learned that there are methods for transferring a model learned on one dataset onto another[3, 7, 8, 9]. By clustering data before classification, we believe the intra-class variations will be reduced.

  1. Choose clustering algorithm
  2. Run data clustering algorithm based on features
  3. Train model on each clustered dataset individually
  4. Build learning network among each model
  5. Run LP-beta algorithm

LP-beta

LP-beta method is proposed by Gehler and Nowozin as follows:

Data

In consistent with Gehler and Nowzin's study, I will apply the method to two data sets: Oxford flowers[4] and Caltech datasets[5, 6]. I might choose some other datasets for preliminary testing.

Timeline

Ultimately, the goals of my project are as follows:

  • 4/08 Project proposal
  • 4/12 Have all data set ready for analysis
  • 4/28 Implement LP-beta algorithm
  • 5/08 Study clustering algorithms
  • 5/08 Project milestone write up
  • 5/29 Implement the hybrid model
  • 5/30 Poster and final write up

    Note: Dates above may be subject to change and certain goals may be shifted forward or backward in time depending on progress.

    References

    1. P. V. Gehler and S. Nowozin, "On Feature Combination Methods for Multiclass Object Classification", IEEE International Conference on Computer Vision (ICCV), 2009.
    2. A. Torralba, and A. Efros, "Unbiased Look at Dataset Bias", IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2011.
    3. A. Bergamo, and L. Torresani, "Exploiting weakly-labeled Web images to improve object classification: a domain adaptation approach", NIPS, 2010.
    4. M.-E. Nilsback and A. Zisserman. A visual vocabulary for flower classification. In ICCV, pages 1447-1454, 2006.
    5. L. Fei-Fei, R. Fergus, and P. Perona, "Learning generative visual models from few training examples: An incremental bayesian approach tested on 101 object categories", In CVPRW, page 178, 2004.
    6. P. V. Gehler and S. Nowozin, "Let the kernel figure it out: Principled learning of pre-processing for kernel classifiers"' In CVPR, 2009.
    7. L. Duan, I.W.-H. Tsang, D. Xu, and S. J. Maybank, "Domain transfer svm for video concept detection", In CVPR, 2009.
    8. K. Saenko, B. Kulis, M. Fritz, and T. Darrell, "Transferring visual category models to new domains", In ECCV, 2010.
    9. J. Yang, R. Yan, and A. G. Hauptmann, "Cross-domain video concept detection using adaptive svms", MULTIMEDIA -07,2007.
    10. C. M. Bishop, Pattern Recognition and Machine Learning. Springer, 1st ed., October 2007.