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I . Problem Improve large-scale retrieval / classification accuracy

This research explores the use of data mining algorithms to improve the accuracy of large-scale retrieval and classification of visual phrases. By incorporating spatial relationships within image features from the Oxford 5K dataset, we employ techniques like Frequent Itemset Mining (FIM) to identify and encode visual phrases that capture distinctive spatial configurations, such as fences, tiles, windows, and arches. Our quantitative and qualitative results demonstrate the effectiveness of these spatial encodings in enhancing classification performance.

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I . Problem Improve large-scale retrieval / classification accuracy

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  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

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