I . Problem Improve large-scale retrieval / classification accuracy - PowerPoint PPT Presentation

jane
slide1 n.
Skip this Video
Loading SlideShow in 5 Seconds..
I . Problem Improve large-scale retrieval / classification accuracy PowerPoint Presentation
Download Presentation
I . Problem Improve large-scale retrieval / classification accuracy

play fullscreen
1 / 1
Download Presentation
I . Problem Improve large-scale retrieval / classification accuracy
101 Views
Download Presentation

I . Problem Improve large-scale retrieval / classification accuracy

- - - - - - - - - - - - - - - - - - - - - - - - - - - E N D - - - - - - - - - - - - - - - - - - - - - - - - - - -
Presentation Transcript

  1. Spatial Encodings for Visual Phrases Using Data Mining Techniques Ivette Carreras HaroonIdrees University of Central Florida (ivette.carreras@knights.ucf.edu) (haroon.idrees@knights.ucf.edu) • IV. Data Mining • Algorithms used in large market basket types of data • VIII. Quantitative Results • VII. Qualitative Results • Phrases capturing fences/tiles (length = 6) • Phrases capturing window/arches (length = 3 & 4) • I. Problem • Improve large-scale retrieval / classification accuracy • Incorporate spatial relationship between the features in the image • Oxford 5K Dataset • II. Approach • Use a mining algorithm to find Frequent Itemsets (phrases) • Use transactions to encode spatial information among features • Different geometric configurations to capture spatial information • Statistics of Phrases Set of items {Beer, Bread, Jelly, Milk, PeanutButter} Transactions • Selection of Phrases - Frequency FIM • Apriori, Eclat • {Bread, PeanutButter} – 3/5 • {Beer, Milk} – 2/5 Frequent Itemsets • Selection of Phrases - Entropy • V. Visual Phrases • Multiple words make up a phrase • Different configurations to capture spatial information • Ranking (Phrases vs. Bag of Words) For each word, find k-NN III. Bag of Visual Words Encode configurations Extract features & quantize 1 • Results Build Bag of Visual Phrases Mine phrases with support s Sort phrases by length Faces • Transaction Format • Four Quadrants • Prefixes : 1000, 2000, 3000, 4000 • 5000 for the origin • Single Circle • No prefixes • Three Circles • Prefixes : 1000, 2000, 3000 • 4000 for the origin Quadrant 2 Quadrant 1 Bikes Extract Regions Compute descriptors Find clusters and frequencies Compute distance matrix Quadrant 4 Quadrant 3 Wild cats References: Single Circle 1. Philbin, J., Chum, O., Isard, M., Sivic, J., and Zisserman, A. Object retrieval with large vocabularies and fast spatial matching. In CVPR (2007). 2. T. Quack, V. Ferrari, and L. Van Gool. Video mining with frequent itemset configurations. In CIVR'06, 2006. 3. Zhang, Y., Jia, Z., Chen, T.: Image retrieval with geometry-preserving visual phrases. In: CVPR. (2011) Circle 3 Circle 2 Circle 1 * http://www.di.ens.fr/willow/events/cvml2010/materials/INRIA_summer_school_2010_Cordelia_ bof_classification.pdf