## 1 Introduction  
Augmented Reality (AR) represents the combination of actual world data and computer-generated virtual three dimensional object data. It is stands on mixed based reality since, it consider both real world and virtual object data. By using the augmented reality user can get experience both realistic and virtual world. Augmented reality has numerous application such as education, medical, museum, e-commerce, construction, tourism, navigation and many more [1; 2; 3]. In this paper, we are considering Mobile Augmented Reality (MAR), which is one of the sub-section of augmented reality. During the last few years, MAR based applications draw attention from both the academy and industry. The main characteristics of a MAR system are: combining virtual and real objects in a real environment, should have real-time interaction, running or displaying the augmented view on a mobile device [4]. On the other hand, Video retrieval refers to retrieving similar videos from the video database by using the queries, where the query can be image or video.  
Mobile Augmented Reality application demands real time interaction, so we cannot apply existing shape based robust features (e.g., SIFT [5] and SURF) that have good matching performance, since these feature descriptor are not computationally efficient for real-time Augmented Reality applications [6]. Moreover, MAR combines the computer generated virtual objects with the real world environment,which cannot be handled by existing texture based feature descriptor (e.g., LBP or LTP), since augmented reality based virtual object can be poorly textured [7; 8].  
So in order to address the above issues, our paper presents a new approach for video retrieval on mobile augmented reality based on a lightweight novel feature descriptor, which extracts the shape features. At first, we extract the top-k (e.g., k = 10 or 15) key frames from the each videos using frame comparison which reduces the computational expenses drastically. Then, we extract the shape features by employing our proposed feature descriptor, Pyramid Ternary Histogram of Oriented Gradients (PTHOG) to bring out prominent information from the virtual objects that are texture-less. After that, we utilize the double-bit quantization [9] based hashing approach to generate the candidate list by searching the nearest neighbors. Finally, similarity measure is performed using cosine similarity to re-rank the videos from the generated candidate list. The notable contribution of our work can be summarized as follows:  
* To the best of our knowledge, this is the first attempt for video retrieval in Mobile Augmented Reality (MAR). Video retrieval is performed on mobile augmented reality environment by using both image and video queries.
* Firstly, we have extracted the top-k key frames from the videos using frame comparison scheme which significantly increases the efficiency.
* Furthermore, we proposed a novel, fast and robust frame based descriptor, namely Pyramid Ternary Histogram of Oriented Gradients (PTHOG), which extract the shape features from the texture-less virtual objects.
* Due to lacking's of video dataset in the area of augmented reality, we also introduced an AVD8 [10] dataset with holograms and augmented reality environments that include both real world and virtual objects.  
The remainder paper is planned as follows. The following section introduces a review of related researches that have been proposed for the augmented reality and video retrieval. In Section 3, we discuss the proposed approach. Section 4 illustrates the dataset employed and experimental analysis. Finally, Section 5 concludes the paper.