1. C OMPUTATION M ODEL FOR V ISUAL C ATEGORIZATION Bhuwan Dhingra

2. O VERVIEW Objective: To study the hierarchy of object categorization using a computational model for vision. Three levels of categorization – super-ordinate, basic and subordinate. Basic level categories – maximize cue validities, and dominate any taxonomy. Categorization implemented in unsupervised manner in the current model.

3. H YPOTHESES Rosch et al, [1], claim that basic level categories accessed first. Marc and Joubert, [2], claim that in a purely visual task super-ordinate categories accessed first. Role of expertise emphasized several times in the literature, [3].

4. T HE M ODEL Bag-of-Features:

5. T HE M ODEL Extracted histograms clustered in an unsupervised manner using k-means algorithm. Distance metric used – (1-correlation(h1,h2)), where h1 and h2 are two histograms.

6. D ATASET 30 images for each subordinate category using Google image search of the keywords.

7. D ATASET Furniture Animal TableChairBirdDog Coffee Table Picnic Table Rocking Chair Bar- stool Crow Pigeon Foxhound Dalmation Super-ordinate classes Basic classes Sub-ordinate classes

8. T ESTS Test 1: Study which type of categorization dominates as the number of detected key-points is varied. Test 2: Study how the performance of the categorization changes with the number of images. Test 3: Study the effect of increasing the number of images of one basic category compared to others Different categorizations were implemented by setting k = 2,4,8.

9. P ERFORMANCE I NDICES Rand Index: TP, TN, FP, FN are true positive and negatives, and false positives and negatives. Purity: Percentage of correctly assigned points, assuming majority class for each cluster. Normalized Mutual Information: Information theoretic mutual information between clusters and classes (normalized to 1). Silhouette Index: Based on the ratio of the within class scatter to between class scatter.

10. R ESULTS Variation of the performance metrics with Peak Threshold or the number of key-points detected.

11. R ESULTS Variation of the performance metrics with Peak Threshold or the number of key-points detected.

12. R ESULTS Variation of the performance metrics with Peak Threshold or the number of key-points detected.

13. R ESULTS Variation of performance metrics with number of images:

14. R ESULTS Effect of expertise Two subordinate and one basic level categories taken together, ex: {{dalmation, foxhound}, bird} Trial 1: Training samples of subordinate categories half of basic category Trial 2: Training samples of subordinate category equal to basic category

15. S OME P ROBLEMS White background images sometimes classified separate from cluttered background. Solution: Foreground extraction High variability in Normalized Mutual Information (NMI) Effect of expertise not clear Solution: Test for exponential increase in images

16. R EFERENCES [1] Rosch, E., Mervis, C., Gray, W., Johnson, D., & Boyes-Braem, P. (1976). Basic objects in natural categories. Cognitive Psychology.Basic objects in natural categories [2] Marc, J.M.M., Joubert, O.R., Nespoulous, J.L. & Fabre-Thorpe, M (2009). The time-course of visual categorizations: you spot the animal faster than the bird. PLoS one.The time-course of visual categorizations: you spot the animal faster than the bird [3] Johnson, K.E., Mervis, C.B. (1997). Effects of varying levels of expertise on the basic level of categorization. Journal of Expert Psychology.Effects of varying levels of expertise on the basic level of categorization