which are of interest to him/her. For instance, a tourist may have a large collection of photos of various places, or a person interested in shopping may take photographs of various shopping malls and shopping items, and so on. Therefore, the photo collection in the smartphone can help give information on the lifestyle patterns of a user. The utilisation of a smartphone camera to monitor daily eating patterns of a user [7], and use of a wearable camera to automatically record images for healthcare [8], are some recent studies in this line of work. It has also been observed that images from online resources, like Flicker, have been used for various purposes like predicting home and vacation locations and for trip recommendations [9; 10; 11]. Interested readers can refer to [12] to obtain a detailed survey on the utilisation of image data from Flicker in various research applications. In contrast, our aim is to focus on personal photograph collections for behaviour analysis rather than relying on external resources. In this paper, we present Lifestyle Pattern MIning (LPaMI), which was introduced and demonstrated at the World IT Show organised in COEX Mall, Seoul, South Korea from 24 to 27 May 2017 as part of G-ITRC project from Kyung Hee University, South Korea. It is a lifestyle pattern mining application to mine intrinsic patterns existing in user smartphone photo collections. We define the lifestyle pattern of a user as a _frequent group of actions performed in different intervals of time_. For example, a group of frequent actions for a person of a tourist nature can be visiting a park, followed by a trip to lake, then to mountains etc., over the weekends in a month. We understand that such lifestyle pattern information with respect to the user can be utilised in various types of personalised recommendation services. Hence, LPaMI can be used to extract the naturally occurring lifestyle patterns followed by a user. The novelty of our proposal lies in the proposal of an end-to-end solution for lifestyle behaviour learning that involves extracting objects of interest (OOIs) from photos, modelling OOIs in a graph format, and graph-based pattern mining. OOIs refer to scenes in photographs related to given application area. For example, areas of application can include tourism, shopping, and eating, while their relevant objects can be outing spots, gift items, and food items, respectively. To model the data in the form of graphs, each extracted OOI is declared as a node and an edge indicates a connection between them, for example, two places visited in a sequence. We then apply various graph mining techniques to discover numerous patterns of a user for lifestyle analysis. The key instinct of LPaMI is that its output i.e., a list of lifestyle patterns, can enhance the functionality of any state-of-the-art recommendation application. For instance, various trip advisor applications contain sufficient information for different type of venues for any given city of the world. Some of them are customizable to provide only relevant types of recommendations like suggesting specific types of restaurants in a city to person who loves eating different types of food. However, the customisation capability of these applications is limited i.e., the user must specify his/her likes and dislikes, personal information, and interests. In this way, they are restricted to making recommendations based on a user's supplied information and do not use his/her patterns followed over the passage of time. We understand that consideration of the lifestyle patterns provides a better overview of the personality traits of the user, which can be used for better recommendations. We show the step-by-step functionalities of LPaMI in Figure 1 as a bird's-eye view to clearly show our concept. We summarise the contributions of our paper as below:  
* We propose mining the lifestyle patterns of a user through his/her personal photo collection in a smartphone. In this way, our goal is to avoid dependency on external resources like GPS and online connectivity.