Towards Learning Semantically Relevant Dictionary for Visual CategoryRecognition

Ashish Gupta, Richard BowdenCentre for Vision, Speech, and Signal Processing, University of Surrey, Guildford, United Kingdom

Objective

Transform feature space rendered by the local patchaffine invariant feature descriptor to a semanticallyrelevant space for visual categorisation.

Challenge

I Large intra-category visual appearance variation.I Training data: insufficient, noisy, background clutter.I Feature descriptor is high-dimensional, sparsely

populated, and renders highly inter-mixed vectors infeature space.

Topic←∑

Words

I Feature space isassumed to havelocal semanticintegrity.

I Intra-categoryappearance varianceameliorated.

Grouping Scattered Clusters

I Analyse Image-Wordco-occurrencestatistics.

I Similar occurrence⇒ semanticequivalence.

I Use co-clustering todiscover wordgroups.

I Group such wordsinto topics.

Multiple Sub-Manifolds

I visual category←∑

object partI visual σ2(object part) is small . d(part1,part2) is large.

Disambiguation by projection to Sub-Manifolds

I Separatinginter-mixeddescriptors.

I Dual objectiveof inter-vectordistance andsub-manifoldembeddingovercomeslimitation ofhardpartitioning.

Influence of Co-clustering

Co-clustering aids grouping of semanticallyequivalent descriptors (similar co-occurrencestatistics or similar sub-manifold embedding) byprojecting from a higher dimensional space (words) tolower dimensional space (topics). This effectivelyreduces separation between equivalent descriptors,verified using a K-NN classifier.

Experiment: Grouping Scattered Clusters

Comparative classification performance (F1 score) ofstandard clustered dictionary (BoW) vs. groupingscattered clusters dictionary for all categories of VOC2010 data set; dictionary size is 1000.

Grouping clusters: different co-clustering methods

Comparison of Information-theoretic (i) andsum-squared Residue (r) co-clustering methods.

Grouping clusters: influence of dictionary size

Topics (100,500,1000,5000)←∑

Words (10,000)Comparative F1 score, averaged for all categories, forvarious datasets.

Experiment: Multiple Sub-Manifold

Comparative classification performance (F1 score) ofstandard clustered dictionary (BoW) vs.multi-manifold dictionary (SSRBC) for all categories ofVOC 2010 data set; dictionary size is 100.

Multi-Manifolds: different co-clustering methods

Comparison of Information-theoretic (i) andsum-squared Residue (r) co-clustering methods.

Towards Semantically Relevant Space

I Group semantically similar small clusters.I Multi-manifolds dictionary.I Prune non-discriminative space.I Combine these paradigms.

Summary

The improvement in classification performancesupports the hypotheses that semantic relevance offeature space can be improved by grouping scatteredtiny clusters based on image-word co-occurrence andlearning a dictionary on multiple sub-manifolds, whichdisambiguates descriptors by projecting them todifferent sub-manifolds. Future work implementspruning non-discriminative space and combine theseparadigms to render a semantically relevant space.

Acknowledgement

Supported by the EU project Dicta-Sign (FP7/2007-2013) underGrant No. 231135 and PASCAL 2.

Center for Vision, Speech, and Signal Processing - University of Surrey - Guildford, United Kingdom Mail: a.gupta@surrey.ac.uk WWW: http://www.ee.surrey.ac.uk/cvssp