An Improved Video Analysis using Context based Extension of LSH

TL;DR

This paper introduces conLSH, a context-aware extension of Locality Sensitive Hashing, improving video sequence retrieval, action recognition accuracy, and enabling automatic annotation of long videos.

Contribution

The paper presents conLSH, a novel context-based hashing scheme integrated with sequence alignment, significantly enhancing video retrieval, recognition accuracy, and video annotation capabilities.

Findings

Achieves over 80% accuracy in video sequence retrieval.

Reduces search space to approximately 42% of the dataset.

Improves action recognition accuracy by 12.83% over existing methods.

Abstract

Locality Sensitive Hashing (LSH) based algorithms have already shown their promise in finding approximate nearest neighbors in high dimen- sional data space. However, there are certain scenarios, as in sequential data, where the proximity of a pair of points cannot be captured without considering their surroundings or context. In videos, as for example, a particular frame is meaningful only when it is seen in the context of its preceding and following frames. LSH has no mechanism to handle the con- texts of the data points. In this article, a novel scheme of Context based Locality Sensitive Hashing (conLSH) has been introduced, in which points are hashed together not only based on their closeness, but also because of similar context. The contribution made in this article is three fold. First, conLSH is integrated with a recently proposed fast optimal sequence alignment algorithm (FOGSAA) using a layered approach. The resultant method is applied to video retrieval for extracting similar sequences. The pro- posed algorithm yields more than 80% accuracy on an average in different datasets. It has been found to save 36.3% of the total time, consumed by the exhaustive search. conLSH reduces the search space to approximately 42% of the entire dataset, when compared with an exhaustive search by the aforementioned FOGSAA, Bag of Words method and the standard LSH implementations. Secondly, the effectiveness of conLSH is demon- strated in action recognition of the video clips, which yields an average gain of 12.83% in terms of classification accuracy over the state of the art methods using STIP descriptors. The last but of great significance is that this article provides a way of automatically annotating long and composite real life videos. The source code of conLSH is made available at http://www.isical.ac.in/~bioinfo_miu/conLSH/conLSH.html